A Minor Project On

“MACHINE LEARNING – BASED DIFFERENTIAL DIAGNOSIS OF

ERYTHEMATO – SQUAMOUS DISEASES FROM CLINICAL
AND MICROSCOPICA FEATURES”
Submitted to
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY, HYDERABAD
In Partial fulfilment of the requirement for the award of degree of
BACHELOR OF TECHNOLOGY
IN
COMPUTER SCIENCE AND ENGINEERING
(ARTIFICIAL INTELLIGENCE & MACHINE LEARNING)

By

K. ADITHI - 21RA1A6603
S. VARSHITHA KUSUMA PRIYA - 21RA1A6606
A. MAHESH - 21RA1A6627

Under the guidance of

MRS.G.SUJATHA
Head of the Department, AI&ML Dept.

Department of Computer Science and Engineering

(Artificial Intelligence & Machine Learning)

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

(ARTIFICIAL INTELLIGENCE & MACHINE LEARNING)
KOMMURI PRATAP REDDY INSTITUTE OF TECHNOLOGY
(Affiliated to JNTUH, Ghanpur(V), Ghatkesar(M), Medchal(D)-500088)
2021 - 2025
 KOMMURI PRATAP REDDY INSTITUTE OF TECHNOLOGY

(Affiliated to JNTUH, Ghanpur(V), Ghatkesar(M), Medchal(D)-500088)

CERTIFICATE

This is to certify that the project work entitled “Machine Learning -

Differential Diagnosis Of Erythemato – Squamous Diseases from Clinical
and Microscopica features” is submitted by Ms.K.ADITHI,
Ms.S.VARSHITHA KUSUMA PRIYA, Mr.A.MAHESH bonafied students of
Kommuri Pratap Reddy Institute of Technology in partial fulfilment of the
requirement for the award of the degree of Bachelor of Technology in
Computer Science and Engineering(Artificial Intelligence & Machine
Learning) of the Jawaharlal Nehru Technological University Hyderabad,
during the year 2024-25

Internal Guide HOD

Mrs.G.Sujatha
Mrs.G.Sujatha

Project Coordinator External Examiner

 DECLARATION

We hereby declare that this project work entitled “Machine Learning - Differential
Diagnosis Of Erythemato – Squamous Diseases from Clinical and Microscopica
features” in partial fulfillment of requirements for the award of degree of Computer
Science and Engineering(Artificial Intelligence & Machine Learning) is a bonafide work
carried out by us during the academic year 2024- 25.

We further declare that this project is a result of our effort and has not been submitted for the
award of any degree by us to any institution .

By
K Adithi Shree (21RA1A6603)
S Varshitha Kusuma Priya (21RA1A6606)
A Mahesh (21RA1A6627)
 ACKNOWLEDGEMENT

It gives us immense pleasure to acknowledge with gratitude, the help and support extended
throughout the project report from the following:

We will be very much grateful to almighty our Parents who have made us capable of
carrying out our job.

We express our profound gratitude to Dr. Ravindra Eklarker, Principal of Kommuri

Pratap Reddy Institute of Technology, who has provided necessary infrastructure and
resources in completing our project report successfully.

We are grateful to Mrs.G.Sujatha who is our Head of the Department, AI&ML for her
amiable ingenious and adept suggestions and pioneering guidance during the project report.

We express our gratitude and thanks to the coordinator Dr. S. Sankar Ganesh of our
department for his contribution for making it success within the given time duration.

We express our deep sense of gratitude and thanks to Internal Guide, Dr. S. Sankar
Ganesh Associate Professor for his guidance during the project report.

We are also very thankful to our Management, Staff Members and all Our Friends for
their valuable suggestions and timely guidance without which we would not have been
completed it.

By

K Adithi Shree (21RA1A6603)

S Varshitha Kusuma Priya (21RA1A6606)
A Mahesh (21RA1A6627)
 Kommuri Pratap Reddy Institute of Technology
Department Of Computer Science And Engineering
(Artificial Intelligence And Machine Learning)

Vision of the Institute

To emerge as a premier institute for high quality professional graduates

who can contribute to economic and social developments of the Nation.

Mission of the Institute

Mission Statement

IM1 To have holistic approach in curriculum and pedagogy

through industry interface to meet the needs of Global
Competency.

IM2 To develop students with knowledge, attitude,

employability skills, entrepreneurship, research
potential and professionally Ethical citizens.

IM3 To contribute to advancement of Engineering &

Technology that would help to satisfy the societal
needs.

IM4 To preserve, promote cultural heritage, humanistic

values and
Spiritual values thus helping in peace and harmony in
the society.
 Kommuri Pratap Reddy Institute of Technology
Department Of Computer Science And Engineering
(Artificial Intelligence And Machine Learning)

Vision of the Department

To Provide Quality Education in Computer Science for the innovative

professionals to work for the development of the nation.

Mission of the Department

Mission Statement

DM1 Laying the path for rich skills in Computer Science

through The basic knowledge of mathematics and
fundamentals of engineering

DM2 Provide latest tools and technology to the students

as a part of learning infrastructure

DM3 Training the students towards employability and

entrepreneurship to meet the societal needs.

DM4 Grooming the students with professional and social

ethics.
 Kommuri Pratap Reddy Institute of Technology
Department Of Computer Science And Engineering
(Artificial Intelligence And Machine Learning)

Program Educational Objectives (PEOs)

PEO’s Statement
PEO1 The graduates of Computer Science and
Engineering will have successful career in
technology.
PEO2 The graduates of the program will have solid
technical and professional foundation to
continue higher studies.
PEO3 The graduate of the program will have skills
to develop products, offer services and
innovation.
PEO4 The graduates of the program will have
fundamental awareness of industry process,
tools and technologies.
 Kommuri Pratap Reddy Institute of Technology
Department Of Computer Science And Engineering
(Artificial Intelligence And Machine Learning)

Program Outcomes

PO1 Engineering Knowledge: Apply the

knowledge of mathematics, science, Engineering
fundamentals, and an engineering specialization to
the solution of complex engineering problems.

PO2 Problem Analysis: Identify, formulate, review

research literature, and analyze complex
engineering problems reaching substantiated
conclusions using first principles of mathematics,
natural sciences, and engineering sciences.

PO3 Design/development of Solutions: Design

solutions for complex engineering problems and
design system components or processes that meet
the specified needs with appropriate consideration
for the public health and safety, and the cultural,
societal, and environmental considerations.

PO4 Conduct investigations of complex

problems: Use research-based knowledge and
research methods including design of experiments,
analysis and interpretation of data, and synthesis of
the information to provide valid conclusions.

PO5 Modern tool usage: Create, select, and apply

appropriate techniques, resources, and modern
engineering and IT tools including prediction and
 modeling to complex engineering activities with an
understanding of the limitations.

PO6 The engineer and society: Apply reasoning

informed by the contextual knowledge to assess
societal, health, safety, legal and cultural issues and
the consequent responsibilities relevant to the
professional engineering practice.

Environment and sustainability: Understand

PO7 the impact of the professional engineering solutions
in societal and environmental context, and
demonstrate the knowledge of, and need for
sustainable development.

PO8 Ethics: Apply ethical principles and commit to

professional ethics and responsibilities and norms of
the engineering practice.

PO9 Individual and team network: Function

effectively as an individual, and as a member or
leader in diverse teams, and in multidisciplinary
settings.
PO10 Communication: Communicate effectively on
complex engineering activities with the engineering
community and with society at large, being able to
comprehend and write effective reports and design
documentation, make Effective presentations, and
give and receive clear instructions.

PO11 Project management and finance:

Demonstrate knowledge and understanding of the
engineering and management principles and apply
these to one’s own work,as a member and leader in
a team, to manage projects and in multidisciplinary
environment.

PO12 Life-Long learning: Recognize the need for,

and have the preparation and able to engage in
independent and life-long learning in the broadest
context of technological change.
PROGRAM SPECIFIC OUTCOMES

PSO1 Foundation of mathematical concepts: To

use mathematical methodologies to crack problem
using suitable mathematical analysis, data structure
and suitable algorithm.
PSO2 Foundation of Computer Science: The
ability to interpret the fundamental concepts and
methodology of computer systems. Students can
understand the functionality of hardware and
software aspects of computer systems.
PSO3 Foundation of Software development: The
ability to grasp the software development lifecycle
and methodologies of software systems. Possess
competent skills and knowledge of software design
process.
 Kommuri Pratap Reddy Institute of Technology
Department Of Computer Science And Engineering
(Artificial Intelligence And Machine Learning)

TABLE OF CONTENTS

ABSTRACT………………………………………………………………

CHAPTER-1 INTRODUCTION………………………………………....
1.1 Overview
1.2 Motivation
1.3 Problem Statement
1.4 Applications

CHAPTER-2 LITERATURE SURVEY…………………………………

CHAPTER-3 EXISTING SYSTEM……………………………………..

CHAPTER-4 PROPOSED SYSTEM……………………………………

4.1 Overview
4.2 Data Preprocessing
4.3 Build and Train ML Model
4.3.1 Decision Tree Classifier
4.3.2 Support Vector Machine
4.3.3 Comparing performance: Why SVM is Superior in this Research
4.4 Advantages

CHAPTER-5 MACHINE LEARNING……………………………………

CHAPTER-6 SOFTWARE ENVIRONMENT……………………………

CHAPTER-7 SOURCE C……………........................................................

CHAPTER-8 RESULTS…………………………………………………...
8.1 Implementation Description:
8.2 Dataset Description
8.3 Results Description

CHAPTER-9 CONCLUSION AND FUTURE SCOPE…………………...

9.1 Conclusion
9.2 Future Scope

REFERENCES…………………………………………………………….
 LIST OF FIGURES

Figure 3.1 Architechture Diagram of Traditional System

Figure 4.1 Proposed block diagram of ML-based erythemato squamous prediction

Figure 8.3.1 First 5 Rows of Dataset

Figure 8.3.2 Count plot of the various types of the class column of the dataset
before applying smote.

Figure 8.3.3 Count plot of the various types of classes of the class column of the
dataset after applying remote.

Figure 8.3.4 Confusion matrix of Decision Tree Algorithm to the dataset.

Figure 8.3.5 Presents the Model Predication on test dataset.

Figure 8.3.6 Performance Metrics of SVM and Decision Tree Classifier Models.
 TABLE OF SCREENSHOTS

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 MACHINE LEARNING BASED DIFFERENTIAL
DIAGONOSIS OF ERYTHEMATO-SQUAMOUS
DISEASES FROM CLINICAL AND MICROSCOPIC
FEATURES

ABSTRACT

Erythemato-squamous diseases, characterized by erythema and scaling, present

significant diagnostic challenges due to their overlapping clinical features and
variability in patient presentations. Traditional diagnostic methods, relying on clinical
examination and histopathological analysis, often involve subjective assessments and
can be time-consuming. Therefore, this is research proposes the development of a
machine learning-based differential diagnosis system to improve the accuracy and
efficiency of diagnosing erythemato-squamous diseases.The system leverages
advanced machine learning algorithms to analyze patient data, including clinical
images and histopathological features, identifying subtle patterns that may be
overlooked by conventional methods. By automating the diagnostic process, the
system aims to provide consistent and accurate differential diagnoses, assisting
dermatologists in making more informed decisions. The machine learning model is
trained on a comprehensive dataset of dermatological cases, enabling it to handle the
complexities and variabilities inherent in erythemato-squamous diseases. The
proposed system addresses several limitations of traditional diagnostic approaches,
such as subjectivity, time consumption, and variability. It enhances diagnostic
accuracy, reduces the time required for diagnosis, and provides scalable solutions that
can be implemented across various healthcare settings. Additionally, the system
supports personalized treatment plans by enabling timely and precise diagnosis,
ultimately improving patient outcomes and disease management. By integrating
machine learning into the diagnostic workflow, this research aims to advance the field
of dermatology, offering a robust tool to enhance diagnostic precision and efficiency
in managing erythemato-squamous diseases. This innovative approach promises to
transform traditional diagnostic practices, paving the way for improved patient care
and optimized clinical operations.
 CHAPTER 1

INTRODUCTION

1.1 Overview

Erythemato-squamous diseases encompass a range of skin disorders characterized by

redness (erythema) and scaling (squamous) of the skin. These conditions include
psoriasis, seborrheic dermatitis, and eczema. The prevalence of these diseases has
been on the rise over the years. For instance, psoriasis affects approximately 2-3% of
the global population, with estimates suggesting that around 125 million people
worldwide are affected as of 2024. The incidence of psoriasis in Europe and North
America is particularly high, with rates reaching up to 4% in some populations.
Seborrheic dermatitis affects around 1-3% of the general population, with higher
prevalence observed in those with compromised immune systems. Eczema, including
atopic dermatitis, affects approximately 10-20% of children and 1-3% of adults
globally, with increasing cases reported in both developed and developing countries.

Recent studies have highlighted a concerning trend in the increasing incidence of

these conditions, partially due to environmental factors, lifestyle changes, and better
diagnostic capabilities. For example, a study conducted in 2022 showed a 15%
increase in diagnosed cases of psoriasis over the past decade, attributed to both
increased awareness and environmental factors. The rising number of cases
underscores the need for more effective diagnostic and management systems to
address these chronic conditions.

1.2 Motivation

Patients suffering from erythemato-squamous diseases often face significant

challenges that impact their quality of life. These conditions can lead to severe
physical discomfort, including itching, pain, and skin lesions, which can cause
emotional distress and social stigma. For many, managing symptoms requires
ongoing medical treatment and lifestyle adjustments, which can be burdensome. For
instance, psoriasis patients may experience exacerbations triggered by stress, diet, or
infections, making symptom management complex and variable.
 Healthcare professionals also face difficulties in diagnosing and treating these
conditions due to their heterogeneous nature. Accurate diagnosis often requires a
combination of clinical evaluation, medical history, and sometimes skin biopsies. The
lack of standardized diagnostic tools and treatment protocols can lead to delays in
diagnosis and suboptimal treatment outcomes. Furthermore, the variability in patient
responses to treatments adds to the challenge, highlighting the need for more
advanced diagnostic and management systems to improve patient care and outcomes.

1.3 Problem Statement

Manual approaches to diagnosing and managing erythemato-squamous diseases are

fraught with challenges. Traditional methods often rely heavily on visual inspection
and patient-reported symptoms, which can be subjective and inconsistent. This
approach can lead to misdiagnosis or delayed diagnosis, as well as variability in
treatment efficacy. Additionally, the manual tracking of disease progression and
treatment response can be cumbersome, leading to gaps in patient data and less
effective monitoring.

Automation in the form of advanced diagnostic tools and systems can address these
issues by providing more accurate, consistent, and timely information. Automated
systems can integrate data from various sources, such as electronic health records,
imaging, and patient inputs, to provide a comprehensive view of a patient’s condition.
This approach not only enhances diagnostic accuracy but also streamlines treatment
planning and monitoring, ultimately leading to improved patient outcomes and more
efficient use of healthcare resources.

1.4 Applications

 Telemedicine Platforms: Enable remote consultations and monitoring, allowing

patients to receive expert care from their homes. These platforms can integrate
image analysis and patient data to support remote diagnosis and treatment planning.

 Clinical Decision Support Systems (CDSS): Assist healthcare professionals in

making informed decisions by providing evidence-based recommendations and
alerts based on patient data and clinical guidelines.
 Mobile Health Apps: Offer tools for patients to track their symptoms, medication
adherence, and disease progression. These apps can provide personalized treatment
reminders and health tips based on real-time data.

 AI-Powered Diagnostic Tools: Use machine learning algorithms to analyze images

and patient data, improving diagnostic accuracy and identifying patterns that may
not be apparent through manual methods.

 Patient Education Platforms: Provide resources and information about

erythemato-squamous diseases, including treatment options and self-care strategies,
helping patients better manage their conditions and make informed decisions about
their health..
 CHAPTER 2

LITERATURE SURVEY

Alshamrani et al. [1] conducted a comprehensive survey on deep learning techniques

for skin disease diagnosis. They explored various neural network architectures,
including convolutional neural networks (CNNs) and their application in dermatology.
The study highlighted the potential of deep learning models to improve diagnostic
accuracy by leveraging large-scale datasets of skin images. The authors also discussed
challenges such as data imbalance and the need for robust validation techniques to
ensure reliable performance in real-world scenarios.

Xie et al. [2] proposed a CNN-based approach for the classification of skin disease
images, focusing on enhancing the performance of automated diagnostic systems.
They demonstrated the effectiveness of deep learning models in distinguishing
between different skin conditions by utilizing a large dataset of clinical images. The
study emphasized the importance of feature extraction and model training on diverse
data to achieve high classification accuracy and address the variability in skin disease
presentations.

Wong et al. [3] developed an automated skin cancer detection system using deep
CNNs. The paper highlighted the system’s ability to classify skin lesions into benign
or malignant categories with high accuracy. The authors addressed the integration of
ML models into clinical workflows, emphasizing the benefits of reduced diagnostic
time and improved consistency. The study also identified limitations, including the
need for extensive and diverse training datasets to enhance model generalization.

Ghosh et al. [4] introduced an intelligent system for automated skin disease diagnosis
utilizing deep learning techniques. The study focused on integrating image analysis
with histopathological data to improve diagnostic precision. The authors highlighted
the system’s capability to handle complex disease features and reduce the reliance on
manual interpretation. The research underscored the system’s potential to assist
dermatologists in making informed decisions and improving patient outcomes.

Kumar et al. [5] proposed a multi-modal deep learning network for dermatological
disease diagnosis, combining clinical images and histopathological data. The paper
demonstrated how the integration of multiple data sources enhances diagnostic
performance by capturing a comprehensive view of the disease. The authors discussed
the advantages of their approach in providing accurate and consistent diagnoses and
the challenges related to data fusion and model training.

Sharma et al. [6] reviewed various machine learning approaches for skin lesion
classification, focusing on the effectiveness of different algorithms in detecting skin
diseases. The study highlighted advancements in ML techniques, such as support
vector machines and neural networks, and their application in dermatology. The
authors addressed the issues of dataset quality and the need for robust evaluation
metrics to ensure the reliability of automated diagnostic systems.

Lee et al. [7] developed hybrid deep learning models for accurate skin disease
classification, incorporating CNNs with other ML techniques. The study
demonstrated the benefits of combining different models to improve diagnostic
accuracy and address limitations of single-model approaches. The authors emphasized
the importance of model integration and the use of extensive datasets to enhance the
system’s ability to handle diverse skin conditions.

Zhao et al. [8] investigated the use of Generative Adversarial Networks (GANs)
alongside CNNs for skin disease diagnosis. The paper focused on how GANs can
generate synthetic images to augment training datasets and improve model
performance. The study highlighted the potential of combining GANs with CNNs to
address challenges such as data scarcity and model overfitting, enhancing the overall
diagnostic accuracy.

Das et al. [9] explored advanced techniques for skin disease detection using machine
learning, including feature selection and model optimization strategies. The study
examined the effectiveness of different ML algorithms in improving diagnostic
accuracy and handling complex skin conditions. The authors discussed the impact of
algorithmic improvements on the reliability of automated systems and the importance
of continuous model refinement.

Smith et al. [10] conducted a systematic review of deep learning applications in

dermatology, focusing on the integration of ML models into clinical practice. The
paper summarized recent advancements in automated skin disease diagnosis and
highlighted key challenges, such as data quality and model interpretability. The
authors emphasized the potential of deep learning to revolutionize dermatological
diagnostics and improve patient care through enhanced accuracy and efficiency.

Johnson et al. [11] reviewed machine learning techniques for automated

dermatological diagnosis, highlighting the development and application of various
algorithms. The study focused on the effectiveness of ML in diagnosing skin
disorders and improving diagnostic precision. The authors addressed challenges such
as dataset limitations and the need for robust validation to ensure accurate and reliable
performance in clinical settings.

Li et al. [12] presented a deep learning-based diagnostic system specifically for

erythemato-squamous diseases. The paper detailed the use of advanced ML models to
analyze clinical images and histopathological data, demonstrating improvements in
diagnostic accuracy and efficiency. The study emphasized the system’s ability to
handle the complexity of erythemato-squamous diseases and its potential to enhance
dermatological practice

Collins et al. [13] investigated predictive modeling techniques for skin disease
classification using machine learning algorithms. The study explored various ML
approaches and their application in diagnosing dermatological conditions,
highlighting the advantages of predictive modeling in improving diagnostic outcomes.
The authors discussed the impact of algorithmic advancements on the accuracy and
efficiency of skin disease diagnosis.

Williams et al. [14] focused on the application of convolutional neural networks for
automated dermatological diagnosis. The paper demonstrated the effectiveness of
CNNs in analyzing skin images and identifying various skin conditions. The study
highlighted the benefits of using deep learning models to enhance diagnostic accuracy
and reduce the time required for manual analysis.

Patel et al. [15] examined the integration of machine learning into dermatological
diagnostic processes, addressing the challenges and prospects of automated systems.
The study explored the potential of ML models to improve diagnostic precision and
efficiency while identifying key issues such as data quality and model interpretability.
The authors emphasized the transformative potential of ML in dermatology and the
need for further research to optimize these systems.
 CHAPTER 3

EXISTING SYSTEM

Traditionally, the diagnosis of erythemato-squamous diseases, such as psoriasis,

seborrheic dermatitis, and lichen planus, relies heavily on clinical examination and
histopathological analysis. Healthcare professionals, primarily dermatologists, utilize
a combination of visual inspection, patient history, and microscopic examination of
skin samples to determine the condition. This approach has been the cornerstone of
dermatological diagnostics for decades.

Figure Architecture 3.1: Diagram of Traditional System.

Process:

1. Clinical Examination:

o Visual Inspection: Dermatologists assess the physical appearance of skin

lesions. They evaluate the distribution, morphology, and characteristics of
the lesions. Key features like redness, scaling, and lesion shape are examined.
 o Patient History: Information about the patient’s symptoms, onset, duration,
and previous treatments is collected. Family history and any related health
conditions are also considered.

2. Histopathological Analysis:

o Biopsy: If a definitive diagnosis is needed, a skin biopsy is performed to

obtain a sample of the affected skin.

o Microscopic Examination: The biopsy sample is examined under a

microscope to identify characteristic histological features associated with
specific diseases. For instance, psoriasis often shows hyperkeratosis and
acanthosis.

Limitations of Traditional Systems:

1. Subjectivity:

o Visual Inspection: The diagnosis heavily relies on the dermatologist’s

experience and expertise. Visual assessment can be subjective, and different
clinicians may interpret the same lesions differently.

o Histopathology: While histopathological analysis is more objective, it still

requires interpretation by a pathologist, and there can be variability in
diagnosing subtle or atypical cases.

2. Time-Consuming:

o Diagnosis Process: The process of diagnosis, from initial consultation to

biopsy and final diagnosis, can be time-consuming. The waiting time for
biopsy results can delay treatment.

o Training Requirements: Dermatologists and pathologists need extensive

training to accurately diagnose erythemato-squamous diseases, which adds to
the time and resource requirements.

3. Resource Intensive:
 o Laboratory Resources: Histopathological analysis requires specialized
equipment and skilled personnel. This can be resource-intensive, especially
in settings with limited access to advanced diagnostic tools.

o Cost: The cost associated with biopsies and subsequent analyses can be high,
making it less accessible to patients in low-resource settings.

4. Diagnostic Accuracy:

o Overlap of Symptoms: Many erythemato-squamous diseases have

overlapping symptoms, making differentiation challenging. For example,
psoriasis and seborrheic dermatitis may present with similar scaly lesions,
complicating the diagnosis.

o Atypical Cases: Some cases may present atypically, making them harder to
diagnose with traditional methods. Misdiagnosis or delayed diagnosis can
lead to inappropriate treatment.

5. Patient Involvement:

o Limited Patient Interaction: Traditional methods may not fully consider

the patient’s subjective experience and preferences. This can impact patient
satisfaction and adherence to treatment.

6. Advancements in Technology:

o Slow Adoption: Traditional systems often lag in incorporating technological

advancements. The integration of digital imaging, artificial intelligence, and
machine learning into diagnostic processes has been slow, limiting the
potential for more accurate and efficient diagnostics.
 CHAPTER 4

PROPOSED SYSTEM

4.1 Overview

Step 1 Dataset: The research utilizes a dataset (data.csv) containing clinical and
microscopic features for diagnosing erythemato-squamous diseases. The dataset
includes various attributes relevant to the diagnosis and a target class representing the
disease type.

Step 2 Data Preprocessing: Initial preprocessing involves loading the dataset and
inspecting it for unique class values and missing values. Missing values are handled
by replacing placeholders (e.g., '?') with NaN and then dropping rows with missing
data. The dataset is subsequently described to understand its statistical properties.

Step 3 Handling Missing Values: Missing values are managed by replacing '?' with
NaN and removing records with any missing entries. This approach ensures that the
dataset is clean and ready for analysis, reducing potential biases and inaccuracies in
the model training process.

Step 4 Data Balancing: To address class imbalance, the Synthetic Minority Over-
sampling Technique (SMOTE) is applied. SMOTE generates synthetic samples for
underrepresented classes to balance the dataset and improve the model's ability to
generalize across all classes.

Step 5 Splitting Data: The dataset is divided into training and testing subsets using
an 80-20 split ratio. This separation allows for model training on a portion of the data
while evaluating its performance on unseen test data.

Step 6 Model Training: Two machine learning models such as Decision Tree
Classifier and Support Vector Machine (SVM) are trained.

 Decision Tree Classifier: The Decision Tree Classifier, with a maximum depth of 3,
is used to classify the data. It is trained on the training set and evaluated using
various metrics including precision, recall, F1 score, and accuracy.
 SVM Classifier: The Support Vector Machine (SVM) model, which is an advanced
algorithm compared to decision trees, is trained using a linear kernel. The SVM
 model is intended to provide better performance by effectively handling the
complexities and non-linearities in the data.

Step 7 Performance Evaluation: Both models are evaluated based on performance

metrics such as precision, recall, F1 score, and accuracy. Confusion matrices and
classification reports are generated to compare their performance. The SVM model is
expected to show improvements over the Decision Tree Classifier in terms of
accuracy and generalization.

Figure 4.1: Proposed block diagram of ML-based erythemato squamous prediction.

4.2 Data Preprocessing

The preprocessing stage is meticulously designed to prepare the data for machine
learning models. Key actions include handling missing data, encoding categorical
variables, visualizing class distribution, addressing class imbalance with SMOTE, and
splitting the data into training and testing sets. These steps are crucial in ensuring that
the data is clean, balanced, and well-structured, enabling the model to learn
effectively and make accurate predictions. This careful preprocessing lays a robust
foundation for applying machine learning algorithms, ultimately leading to better
diagnostic tools for erythemato-squamous diseases.
Loading the Data: The dataset is loaded from a CSV file into a Pandas DataFrame,
which is a common structure for handling tabular data in Python. This step is
fundamental, as it provides the raw data needed for analysis.

Exploration: The dataset is then explored to understand its structure. The unique
values in the target column (class) are identified to check the different categories of
erythemato-squamous diseases. The head() function displays the first few rows of the
dataset, giving an initial look at the data. The info() function provides a summary of
the dataset, including the data types of each column, the number of non-null entries,
and memory usage.

Missing Values Check: Missing values in the dataset are identified using
isnull().sum(), which counts the number of null entries in each column. Missing data
can pose a significant challenge, leading to biases or errors if not handled properly.

Replacing Missing Values: Any placeholders for missing values, such as '?', are
replaced with NaN (Not a Number) to standardize the dataset and make it easier to
handle missing data.

Dropping Missing Data: Rows containing missing values are removed using
dropna(). This step is crucial because most machine learning algorithms require a
complete dataset without any missing values. Dropping rows with missing values
ensures that the remaining dataset is clean and consistent.

Data visualization: A count plot is generated to visualize the distribution of different

disease classes within the dataset. This visualization helps identify any class
imbalance, which is common in medical datasets where some diseases may be more
prevalent than others. Understanding the class distribution is vital for guiding the next
steps in preprocessing, particularly in dealing with imbalances.

Label Encoding: The target variable (class), which is categorical, is converted into
numerical values using LabelEncoder. This encoding is necessary because machine
learning models typically require numerical input. Each class is assigned a unique
integer value, making it easier for the model to process and differentiate between the
categories.

Defining Independent and Dependent Variables: The dataset is split into features
(independent variables) and the target (dependent variable). The features (X) include
 all columns except the last one, which is the target variable (y). This separation is
essential for supervised learning tasks, where the model learns to map inputs (X) to
outputs (y).

SMOTE (Synthetic Minority Over-sampling Technique): Class imbalance, where

some classes are underrepresented compared to others, can lead to biased model
predictions. SMOTE is applied to generate synthetic examples for the minority class,
effectively balancing the class distribution. By using SMOTE, the model can learn
equally from all classes, improving its ability to generalize and make accurate
predictions across different categories.

Train-Test Split: The dataset is split into training and testing sets using
train_test_split(). Typically, 80% of the data is used for training the model, while the
remaining 20% is reserved for testing. This split is essential for evaluating the
model’s performance on unseen data, providing a realistic assessment of how well the
model is likely to perform in real-world scenarios.

4.3 Build and Train ML Model

4.3.1 Decision Tree Classifier:

A Decision Tree Classifier is a popular machine learning algorithm used for both
classification and regression tasks. It works by splitting the dataset into subsets based
on the value of the input features. The model creates a tree-like structure where each
internal node represents a decision on a feature, each branch represents the outcome
of the decision, and each leaf node represents a class label.

How It Works:

 Splitting Criteria: The tree is built by selecting the best features to split the data at
each node. The goal is to make each subset as pure as possible, meaning that the
data in each subset should ideally belong to the same class. Common metrics for
selecting the best split include Gini Impurity and Information Gain.

 Tree Construction: The process continues recursively, splitting the data further
until the tree reaches a predefined depth or until the data within a node is pure
enough (i.e., all samples belong to the same class).
 Prediction: For a new input, the model traverses the tree by following the decisions
made at each node, eventually arriving at a leaf node that gives the predicted class.

Advantages:

 Interpretability: Decision trees are easy to understand and interpret, making them
useful for explaining predictions.

 Handling Non-linear Relationships: They can capture non-linear relationships

between features.

Limitations:

 Overfitting: Decision trees can easily overfit, especially if they are allowed to grow
too deep, capturing noise rather than the underlying pattern in the data.

 Instability: Small changes in the data can lead to a completely different tree
structure, making them less robust.

4.3.2 Support Vector Machine

Support Vector Machine is a powerful and versatile classification algorithm that

works well for high-dimensional datasets. SVM aims to find the optimal hyperplane
that best separates the data into different classes. The "best" hyperplane is the one that
maximizes the margin, which is the distance between the hyperplane and the nearest
data points from each class (called support vectors).

How It Works:

 Hyperplane: In a two-dimensional space, the hyperplane is a line that separates the

two classes. In higher dimensions, it becomes a plane or a hyperplane.

 Maximizing Margin: SVM selects the hyperplane that maximizes the margin
between the two classes. This margin maximization helps in improving the model's
ability to generalize to unseen data.

 Kernel Trick: SVM can be extended to work in non-linear spaces using a technique
called the "kernel trick." The kernel function implicitly maps the input data into a
 higher-dimensional space where it becomes easier to separate the classes with a
linear hyperplane.

Advantages:

 Effective in High Dimensions: SVM performs well in high-dimensional spaces and

is effective even when the number of dimensions exceeds the number of samples.

 Robustness: SVM is less prone to overfitting, particularly in high-dimensional

spaces, and is generally more robust to outliers compared to decision trees.

 Flexibility: The kernel trick allows SVM to model complex non-linear relationships.

4.3.3 Comparing Performance: Why SVM is Superior in This Research

In this research, the Support Vector Machine (SVM) outperformed the Decision Tree
Classifier in terms of key performance metrics such as accuracy, precision, recall, and
F1-score.

Reasons for Superior Performance:

 Handling of High Dimensionality: The dataset for erythemato-squamous diseases

includes numerous clinical and microscopic features. SVM's ability to work
effectively in high-dimensional spaces makes it better suited for such a complex
dataset.

 Robustness to Overfitting: While Decision Trees are prone to overfitting, especially

when the tree becomes too deep, SVM mitigates this risk by focusing on
maximizing the margin, leading to a more generalized model.

 Kernel Trick: The potential use of a linear kernel in SVM helps in finding the
optimal hyperplane even in cases where the relationship between features is non-
linear, which may not be as effectively captured by a Decision Tree.

In summary, while Decision Trees offer simplicity and interpretability, SVM's

superior ability to handle high-dimensional, complex data with robustness against
overfitting makes it the better choice for the differential diagnosis of erythemato-
squamous diseases in this research. The SVM's superior performance in this context is
a testament to its strength in handling sophisticated classification tasks.
 4.4 Advantages

1. Improved Accuracy: SVMs are known for their ability to achieve higher accuracy,
especially in complex datasets where linear decision boundaries may not suffice.
The linear kernel used in this research helps in effective classification of linearly
separable data.

2. Handling Non-linearity: By using different kernels (e.g., polynomial, RBF), SVMs

can handle non-linear relationships in the data, making them versatile for various
types of classification problems.

3. Robust to Overfitting: SVMs are less prone to overfitting compared to decision

trees, particularly in high-dimensional spaces. The regularization parameter helps in
controlling the model complexity.

4. Effective in High Dimensions: SVMs are effective in high-dimensional spaces,

which is advantageous for datasets with numerous features.

5. Support Vector Importance: The model focuses on the support vectors, which are
the most informative data points for classification, leading to a more efficient and
precise model.

6. Scalability: SVMs are scalable with respect to the number of features and can be
adapted for large datasets using techniques like kernel approximation.

7. Versatility: With different kernel functions, SVMs can be tailored to fit various data
distributions and relationships, providing flexibility in modeling complex patterns.

Overall, the proposed SVM model offers significant improvements over traditional
methods, such as decision trees, by enhancing classification accuracy and handling
complex datasets more effectively.
 CHAPTER 5

MACHINE LEARNING

What is Machine Learning

Before we take a look at the details of various machine learning methods, let's start by
looking at what machine learning is, and what it isn't. Machine learning is often
categorized as a subfield of artificial intelligence, but I find that categorization can
often be misleading at first brush. The study of machine learning certainly arose from
research in this context, but in the data science application of machine learning
methods, it's more helpful to think of machine learning as a means of building models
of data.

Fundamentally, machine learning involves building mathematical models to help

understand data. "Learning" enters the fray when we give these models tunable
parameters that can be adapted to observed data; in this way the program can be
considered to be "learning" from the data. Once these models have been fit to
previously seen data, they can be used to predict and understand aspects of newly
observed data. I'll leave to the reader the more philosophical digression regarding the
extent to which this type of mathematical, model-based "learning" is similar to the
"learning" exhibited by the human brain. Understanding the problem setting in
machine learning is essential to using these tools effectively, and so we will start with
some broad categorizations of the types of approaches we'll discuss here.

Categories of Machine Leaning

At the most fundamental level, machine learning can be categorized into two main
types: supervised learning and unsupervised learning.

Supervised learning involves somehow modeling the relationship between measured

features of data and some label associated with the data; once this model is
determined, it can be used to apply labels to new, unknown data. This is further
subdivided into classification tasks and regression tasks: in classification, the labels
 are discrete categories, while in regression, the labels are continuous quantities. We
will see examples of both types of supervised learning in the following section.

Unsupervised learning involves modeling the features of a dataset without reference

to any label and is often described as "letting the dataset speak for itself." These
models include tasks such as clustering and dimensionality reduction. Clustering
algorithms identify distinct groups of data, while dimensionality reduction algorithms
search for more succinct representations of the data. We will see examples of both
types of unsupervised learning in the following section.

Need for Machine Learning

Human beings, at this moment, are the most intelligent and advanced species on earth
because they can think, evaluate, and solve complex problems. On the other side, AI
is still in its initial stage and have not surpassed human intelligence in many aspects.
Then the question is that what is the need to make machine learn? The most suitable
reason for doing this is, “to make decisions, based on data, with efficiency and scale”.

Lately, organizations are investing heavily in newer technologies like Artificial

Intelligence, Machine Learning and Deep Learning to get the key information from
data to perform several real-world tasks and solve problems. We can call it data-
driven decisions taken by machines, particularly to automate the process. These data-
driven decisions can be used, instead of using programing logic, in the problems that
cannot be programmed inherently. The fact is that we can’t do without human
intelligence, but other aspect is that we all need to solve real-world problems with
efficiency at a huge scale. That is why the need for machine learning arises.

Challenges in Machines Learning

While Machine Learning is rapidly evolving, making significant strides with

cybersecurity and autonomous cars, this segment of AI as whole still has a long way
to go. The reason behind is that ML has not been able to overcome number of
challenges. The challenges that ML is facing currently are −

1. Quality of data − Having good-quality data for ML algorithms is one of the biggest
challenges. Use of low-quality data leads to the problems related to data
preprocessing and feature extraction.
2. Time-Consuming task − Another challenge faced by ML models is the consumption
of time especially for data acquisition, feature extraction and retrieval.

3. Lack of specialist persons − As ML technology is still in its infancy stage,

availability of expert resources is a tough job.

4. No clear objective for formulating business problems − Having no clear objective

and well-defined goal for business problems is another key challenge for ML
because this technology is not that mature yet.

5. Issue of overfitting & underfitting − If the model is overfitting or underfitting, it

cannot be represented well for the problem.

6. Curse of dimensionality − Another challenge ML model faces is too many features

of data points. This can be a real hindrance.

7. Difficulty in deployment − Complexity of the ML model makes it quite difficult to

be deployed in real life.

Applications of Machines Learning

Machine Learning is the most rapidly growing technology and according to

researchers we are in the golden year of AI and ML. It is used to solve many real-
world complex problems which cannot be solved with traditional approach. Following
are some real-world applications of ML.

 Emotion analysis

 Sentiment analysis

 Error detection and prevention

 Weather forecasting and prediction

 Stock market analysis and forecasting

 Speech synthesis

 Speech recognition

 Customer segmentation

 Object recognition
 Fraud detection

 Fraud prevention

 Recommendation of products to customer in online shopping

How to Start Learning Machine Learning?

Arthur Samuel coined the term “Machine Learning” in 1959 and defined it as a “Field
of study that gives computers the capability to learn without being explicitly
programmed”.

And that was the beginning of Machine Learning! In modern times, Machine
Learning is one of the most popular (if not the most!) career choices. According
to Indeed, Machine Learning Engineer Is the Best Job of 2019 with a 344% growth
and an average base salary of $146,085 per year.

But there is still a lot of doubt about what exactly is Machine Learning and how to
start learning it? So, this article deals with the Basics of Machine Learning and also
the path you can follow to eventually become a full-fledged Machine Learning
Engineer. Now let’s get started!!!

How to start learning ML?

This is a rough roadmap you can follow on your way to becoming an insanely talented
Machine Learning Engineer. Of course, you can always modify the steps according to
your needs to reach your desired end-goal!

Step 1 – Understand the Prerequisites

In case you are a genius, you could start ML directly but normally, there are some
prerequisites that you need to know which include Linear Algebra, Multivariate
Calculus, Statistics, and Python. And if you don’t know these, never fear! You don’t
need a Ph.D. degree in these topics to get started but you do need a basic
understanding.

(a) Learn Linear Algebra and Multivariate Calculus

Both Linear Algebra and Multivariate Calculus are important in Machine Learning.
However, the extent to which you need them depends on your role as a data scientist.
If you are more focused on application heavy machine learning, then you will not be
 that heavily focused on maths as there are many common libraries available. But if
you want to focus on R&D in Machine Learning, then mastery of Linear Algebra and
Multivariate Calculus is very important as you will have to implement many ML
algorithms from scratch.

(b) Learn Statistics

Data plays a huge role in Machine Learning. In fact, around 80% of your time as an
ML expert will be spent collecting and cleaning data. And statistics is a field that
handles the collection, analysis, and presentation of data. So it is no surprise that you
need to learn it!!!
Some of the key concepts in statistics that are important are Statistical Significance,
Probability Distributions, Hypothesis Testing, Regression, etc. Also, Bayesian
Thinking is also a very important part of ML which deals with various concepts like
Conditional Probability, Priors, and Posteriors, Maximum Likelihood, etc.

(c) Learn Python

Some people prefer to skip Linear Algebra, Multivariate Calculus and Statistics and
learn them as they go along with trial and error. But the one thing that you absolutely
cannot skip is Python! While there are other languages you can use for Machine
Learning like R, Scala, etc. Python is currently the most popular language for ML. In
fact, there are many Python libraries that are specifically useful for Artificial
Intelligence and Machine Learning such as Keras, TensorFlow, Scikit-learn, etc.

So, if you want to learn ML, it’s best if you learn Python! You can do that using
various online resources and courses such as Fork Python available Free on
GeeksforGeeks.

Step 2 – Learn Various ML Concepts

Now that you are done with the prerequisites, you can move on to actually learning
ML (Which is the fun part!!!) It’s best to start with the basics and then move on to the
more complicated stuff. Some of the basic concepts in ML are:

(a) Terminologies of Machine Learning

 Model – A model is a specific representation learned from data by applying some

machine learning algorithm. A model is also called a hypothesis.
 Feature – A feature is an individual measurable property of the data. A set of
numeric features can be conveniently described by a feature vector. Feature vectors
are fed as input to the model. For example, in order to predict a fruit, there may be
features like color, smell, taste, etc.

 Target (Label) – A target variable or label is the value to be predicted by our model.
For the fruit example discussed in the feature section, the label with each set of input
would be the name of the fruit like apple, orange, banana, etc.

 Training – The idea is to give a set of inputs(features) and it’s expected

outputs(labels), so after training, we will have a model (hypothesis) that will then
map new data to one of the categories trained on.

 Prediction – Once our model is ready, it can be fed a set of inputs to which it will
provide a predicted output(label).

(b) Types of Machine Learning

 Supervised Learning – This involves learning from a training dataset with labeled
data using classification and regression models. This learning process continues
until the required level of performance is achieved.

 Unsupervised Learning – This involves using unlabelled data and then finding the
underlying structure in the data in order to learn more and more about the data itself
using factor and cluster analysis models.

 Semi-supervised Learning – This involves using unlabelled data like Unsupervised

Learning with a small amount of labeled data. Using labeled data vastly increases
the learning accuracy and is also more cost-effective than Supervised Learning.

 Reinforcement Learning – This involves learning optimal actions through trial and
error. So, the next action is decided by learning behaviors that are based on the
current state and that will maximize the reward in the future.

Advantages of Machine learning

1. Easily identifies trends and patterns: Machine Learning can review large volumes
of data and discover specific trends and patterns that would not be apparent to humans.
For instance, for an e-commerce website like Amazon, it serves to understand the
browsing behaviors and purchase histories of its users to help cater to the right
products, deals, and reminders relevant to them. It uses the results to reveal relevant
advertisements to them.

2. No human intervention needed (automation): With ML, you don’t need to babysit
your research every step of the way. Since it means giving machines the ability to
learn, it lets them make predictions and also improve the algorithms on their own. A
common example of this is anti-virus softwares; they learn to filter new threats as
they are recognized. ML is also good at recognizing spam.

3. Continuous Improvement: As ML algorithms gain experience, they keep improving

in accuracy and efficiency. This lets them make better decisions. Say you need to
make a weather forecast model. As the amount of data, you have keeps growing, your
algorithms learn to make more accurate predictions faster.

4. Handling multi-dimensional and multi-variety data: Machine Learning algorithms

are good at handling data that are multi-dimensional and multi-variety, and they can
do this in dynamic or uncertain environments.

5. Wide Applications: You could be an e-tailer or a healthcare provider and make ML

work for you. Where it does apply, it holds the capability to help deliver a much more
personal experience to customers while also targeting the right customers.

Disadvantages of Machine Learning

1. Data Acquisition: Machine Learning requires massive data sets to train on, and
these should be inclusive/unbiased, and of good quality. There can also be times
where they must wait for new data to be generated.

2. Time and Resources: ML needs enough time to let the algorithms learn and develop
enough to fulfill their purpose with a considerable amount of accuracy and relevancy.
It also needs massive resources to function. This can mean additional requirements of
computer power for you.

3. Interpretation of Results: Another major challenge is the ability to accurately

interpret results generated by the algorithms. You must also carefully choose the
algorithms for your purpose.
4. High error-susceptibility: Machine Learning is autonomous but highly susceptible
to errors. Suppose you train an algorithm with data sets small enough to not be
inclusive. You end up with biased predictions coming from a biased training set. This
leads to irrelevant advertisements being displayed to customers. In the case of ML,
such blunders can set off a chain of errors that can go undetected for long periods of
time. And when they do get noticed, it takes quite some time to recognize the source
of the issue, and even longer to correct it.
 CHAPTER 6

SOFTWARE ENVIRONMENT

What is Python?

Below are some facts about Python.

 Python is currently the most widely used multi-purpose, high-level programming

language.

 Python allows programming in Object-Oriented and Procedural paradigms. Python

programs generally are smaller than other programming languages like Java.

 Programmers have to type relatively less and indentation requirement of the

language, makes them readable all the time.

 Python language is being used by almost all tech-giant companies like – Google,
Amazon, Facebook, Instagram, Dropbox, Uber… etc.

The biggest strength of Python is huge collection of standard libraries which can be
used for the following –

 Machine Learning

 GUI Applications (like Kivy, Tkinter, PyQt etc.)

 Web frameworks like Django (used by YouTube, Instagram, Dropbox)

 Image processing (like Opencv, Pillow)

 Web scraping (like Scrapy, BeautifulSoup, Selenium)

 Test frameworks

 Multimedia

Advantages of Python

Let’s see how Python dominates over other languages.

1. Extensive Libraries
 Python downloads with an extensive library and it contain code for various purposes
like regular expressions, documentation-generation, unit-testing, web browsers,
threading, databases, CGI, email, image manipulation, and more. So, we don’t have to
write the complete code for that manually.

2. Extensible

As we have seen earlier, Python can be extended to other languages. You can write
some of your code in languages like C++ or C. This comes in handy, especially in
researchs.

3. Embeddable

Complimentary to extensibility, Python is embeddable as well. You can put your

Python code in your source code of a different language, like C++. This lets us
add scripting capabilities to our code in the other language.

4. Improved Productivity

The language’s simplicity and extensive libraries render programmers more

productive than languages like Java and C++ do. Also, the fact that you need to write
less and get more things done.

5. IOT Opportunities

Since Python forms the basis of new platforms like Raspberry Pi, it finds the future
bright for the Internet of Things. This is a way to connect the language with the real
world.

6. Simple and Easy

When working with Java, you may have to create a class to print ‘Hello World’. But
in Python, just a print statement will do. It is also quite easy to learn, understand,
and code. This is why when people pick up Python, they have a hard time adjusting to
other more verbose languages like Java.

7. Readable

Because it is not such a verbose language, reading Python is much like reading
English. This is the reason why it is so easy to learn, understand, and code. It also
 does not need curly braces to define blocks, and indentation is mandatory. These
further aids the readability of the code.

8. Object-Oriented

This language supports both the procedural and object-oriented programming

paradigms. While functions help us with code reusability, classes and objects let us
model the real world. A class allows the encapsulation of data and functions into one.

9. Free and Open-Source

Like we said earlier, Python is freely available. But not only can you download
Python for free, but you can also download its source code, make changes to it, and
even distribute it. It downloads with an extensive collection of libraries to help you
with your tasks.

10. Portable

When you code your research in a language like C++, you may need to make some
changes to it if you want to run it on another platform. But it isn’t the same with
Python. Here, you need to code only once, and you can run it anywhere. This is
called Write Once Run Anywhere (WORA). However, you need to be careful enough
not to include any system-dependent features.

11. Interpreted

Lastly, we will say that it is an interpreted language. Since statements are executed
one by one, debugging is easier than in compiled languages.

Any doubts till now in the advantages of Python? Mention in the comment section.

Advantages of Python Over Other Languages

1. Less Coding

Almost all of the tasks done in Python requires less coding when the same task is
done in other languages. Python also has an awesome standard library support, so you
 don’t have to search for any third-party libraries to get your job done. This is the
reason that many people suggest learning Python to beginners.

2. Affordable

Python is free therefore individuals, small companies or big organizations can leverage
the free available resources to build applications. Python is popular and widely used so
it gives you better community support.

The 2019 Github annual survey showed us that Python has overtaken Java in the most
popular programming language category.

3. Python is for Everyone

Python code can run on any machine whether it is Linux, Mac or Windows.
Programmers need to learn different languages for different jobs but with Python, you
can professionally build web apps, perform data analysis and machine learning,
automate things, do web scraping and also build games and powerful visualizations. It
is an all-rounder programming language.

Disadvantages of Python

So far, we’ve seen why Python is a great choice for your research. But if you choose it,
you should be aware of its consequences as well. Let’s now see the downsides of
choosing Python over another language.

1. Speed Limitations

We have seen that Python code is executed line by line. But since Python is
interpreted, it often results in slow execution. This, however, isn’t a problem unless
speed is a focal point for the research. In other words, unless high speed is a
requirement, the benefits offered by Python are enough to distract us from its speed
limitations.

2. Weak in Mobile Computing and Browsers

While it serves as an excellent server-side language, Python is much rarely seen on

the client-side. Besides that, it is rarely ever used to implement smartphone-based
applications. One such application is called Carbonnelle.
The reason it is not so famous despite the existence of Brython is that it isn’t that
secure.

3. Design Restrictions

As you know, Python is dynamically-typed. This means that you don’t need to declare
the type of variable while writing the code. It uses duck-typing. But wait, what’s that?
Well, it just means that if it looks like a duck, it must be a duck. While this is easy on
the programmers during coding, it can raise run-time errors.

4. Underdeveloped Database Access Layers

Compared to more widely used technologies like JDBC (Java DataBase

Connectivity) and ODBC (Open DataBase Connectivity), Python’s database access
layers are a bit underdeveloped. Consequently, it is less often applied in huge
enterprises.

5. Simple

No, we’re not kidding. Python’s simplicity can indeed be a problem. Take my
example. I don’t do Java, I’m more of a Python person. To me, its syntax is so simple
that the verbosity of Java code seems unnecessary.

This was all about the Advantages and Disadvantages of Python Programming
Language.

History of Python

What do the alphabet and the programming language Python have in common? Right,
both start with ABC. If we are talking about ABC in the Python context, it's clear that
the programming language ABC is meant. ABC is a general-purpose programming
language and programming environment, which had been developed in the
Netherlands, Amsterdam, at the CWI (Centrum Wiskunde &Informatica). The
greatest achievement of ABC was to influence the design of Python. Python was
conceptualized in the late 1980s. Guido van Rossum worked that time in a research at
the CWI, called Amoeba, a distributed operating system. In an interview with Bill
Venners1, Guido van Rossum said: "In the early 1980s, I worked as an implementer
on a team building a language called ABC at Centrum voor Wiskunde en Informatica
(CWI). I don't know how well people know ABC's influence on Python. I try to
 mention ABC's influence because I'm indebted to everything I learned during that
research and to the people who worked on it. "Later on in the same Interview, Guido
van Rossum continued: "I remembered all my experience and some of my frustration
with ABC. I decided to try to design a simple scripting language that possessed some
of ABC's better properties, but without its problems. So, I started typing. I created a
simple virtual machine, a simple parser, and a simple runtime. I made my own version
of the various ABC parts that I liked. I created a basic syntax, used indentation for
statement grouping instead of curly braces or begin-end blocks, and developed a small
number of powerful data types: a hash table (or dictionary, as we call it), a list, strings,
and numbers."

Python Development Steps

Guido Van Rossum published the first version of Python code (version 0.9.0) at
alt.sources in February 1991. This release included already exception handling,
functions, and the core data types of lists, dict, str and others. It was also object
oriented and had a module system.
Python version 1.0 was released in January 1994. The major new features included in
this release were the functional programming tools lambda, map, filter and reduce,
which Guido Van Rossum never liked. Six and a half years later in October 2000,
Python 2.0 was introduced. This release included list comprehensions, a full garbage
collector and it was supporting unicode. Python flourished for another 8 years in the
versions 2.x before the next major release as Python 3.0 (also known as "Python
3000" and "Py3K") was released. Python 3 is not backwards compatible with Python
2.x. The emphasis in Python 3 had been on the removal of duplicate programming
constructs and modules, thus fulfilling or coming close to fulfilling the 13th law of the
Zen of Python: "There should be one -- and preferably only one -- obvious way to do
it."Some changes in Python 7.3:

 Print is now a function.

 Views and iterators instead of lists

 The rules for ordering comparisons have been simplified. E.g., a heterogeneous list
cannot be sorted, because all the elements of a list must be comparable to each
other.

 There is only one integer type left, i.e., int. long is int as well.

 The division of two integers returns a float instead of an integer. "//" can be used to
have the "old" behaviour.

 Text Vs. Data Instead of Unicode Vs. 8-bit

Purpose

We demonstrated that our approach enables successful segmentation of intra-retinal

layers—even with low-quality images containing speckle noise, low contrast, and
different intensity ranges throughout—with the assistance of the ANIS feature.

Python

Python is an interpreted high-level programming language for general-purpose

programming. Created by Guido van Rossum and first released in 1991, Python has a
design philosophy that emphasizes code readability, notably using significant
whitespace.

Python features a dynamic type system and automatic memory management. It

supports multiple programming paradigms, including object-oriented, imperative,
functional and procedural, and has a large and comprehensive standard library.

 Python is Interpreted − Python is processed at runtime by the interpreter. You do not

need to compile your program before executing it. This is similar to PERL and PHP.

 Python is Interactive − you can actually sit at a Python prompt and interact with the
interpreter directly to write your programs.

Python also acknowledges that speed of development is important. Readable and terse
code is part of this, and so is access to powerful constructs that avoid tedious
 repetition of code. Maintainability also ties into this may be an all but useless metric,
but it does say something about how much code you have to scan, read and/or
understand to troubleshoot problems or tweak behaviors. This speed of development,
the ease with which a programmer of other languages can pick up basic Python skills
and the huge standard library is key to another area where Python excels. All its tools
have been quick to implement, saved a lot of time, and several of them have later
been patched and updated by people with no Python background - without breaking.

Modules Used in Research

TensorFlow

TensorFlow is a free and open-source software library for dataflow and differentiable
programming across a range of tasks. It is a symbolic math library and is also used
for machine learning applications such as neural networks. It is used for both research
and production at Google.‍

TensorFlow was developed by the Google Brain team for internal Google use. It was
released under the Apache 2.0 open-source license on November 9, 2015.

NumPy

NumPy is a general-purpose array-processing package. It provides a high-

performance multidimensional array object, and tools for working with these arrays.

It is the fundamental package for scientific computing with Python. It contains various
features including these important ones:

 A powerful N-dimensional array object

 Sophisticated (broadcasting) functions

 Tools for integrating C/C++ and Fortran code

 Useful linear algebra, Fourier transform, and random number capabilities

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-
dimensional container of generic data. Arbitrary datatypes can be defined using
NumPy which allows NumPy to seamlessly and speedily integrate with a wide variety
of databases.

Pandas

Pandas is an open-source Python Library providing high-performance data

manipulation and analysis tool using its powerful data structures. Python was majorly
used for data munging and preparation. It had very little contribution towards data
analysis. Pandas solved this problem. Using Pandas, we can accomplish five typical
steps in the processing and analysis of data, regardless of the origin of data load,
prepare, manipulate, model, and analyze. Python with Pandas is used in a wide range
of fields including academic and commercial domains including finance, economics,
Statistics, analytics, etc.

Matplotlib

Matplotlib is a Python 2D plotting library which produces publication quality figures

in a variety of hardcopy formats and interactive environments across platforms.
Matplotlib can be used in Python scripts, the Python and IPython shells,
the Jupyter Notebook, web application servers, and four graphical user interface
toolkits. Matplotlib tries to make easy things easy and hard things possible. You can
generate plots, histograms, power spectra, bar charts, error charts, scatter plots, etc.,
with just a few lines of code. For examples, see the sample plots and thumbnail
gallery.

For simple plotting the pyplot module provides a MATLAB-like interface,

particularly when combined with IPython. For the power user, you have full control
of line styles, font properties, axes properties, etc, via an object-oriented interface or
via a set of functions familiar to MATLAB users.

Scikit – learn

Scikit-learn provides a range of supervised and unsupervised learning algorithms via a

consistent interface in Python. It is licensed under a permissive simplified BSD
license and is distributed under many Linux distributions, encouraging academic and
commercial use. Python
 Python is an interpreted high-level programming language for general-purpose
programming. Created by Guido van Rossum and first released in 1991, Python has a
design philosophy that emphasizes code readability, notably using significant
whitespace.

Python features a dynamic type system and automatic memory management. It

supports multiple programming paradigms, including object-oriented, imperative,
functional and procedural, and has a large and comprehensive standard library.

 Python is Interpreted − Python is processed at runtime by the interpreter. You do not

need to compile your program before executing it. This is similar to PERL and PHP.

 Python is Interactive − you can actually sit at a Python prompt and interact with the
interpreter directly to write your programs.

Python also acknowledges that speed of development is important. Readable and terse
code is part of this, and so is access to powerful constructs that avoid tedious
repetition of code. Maintainability also ties into this may be an all but useless metric,
but it does say something about how much code you have to scan, read and/or
understand to troubleshoot problems or tweak behaviors. This speed of development,
the ease with which a programmer of other languages can pick up basic Python skills
and the huge standard library is key to another area where Python excels. All its tools
have been quick to implement, saved a lot of time, and several of them have later
been patched and updated by people with no Python background - without breaking.

Install Python Step-by-Step in Windows and Mac

Python a versatile programming language doesn’t come pre-installed on your

computer devices. Python was first released in the year 1991 and until today it is a
very popular high-level programming language. Its style philosophy emphasizes code
readability with its notable use of great whitespace.

The object-oriented approach and language construct provided by Python enables

programmers to write both clear and logical code for researchs. This software does
not come pre-packaged with Windows.
How to Install Python on Windows and Mac

There have been several updates in the Python version over the years. The question is
how to install Python? It might be confusing for the beginner who is willing to start
learning Python but this tutorial will solve your query. The latest or the newest
version of Python is version 3.7.4 or in other words, it is Python 3.

Note: The python version 3.7.4 cannot be used on Windows XP or earlier devices.

Before you start with the installation process of Python. First, you need to know about
your System Requirements. Based on your system type i.e., operating system and
based processor, you must download the python version. My system type is
a Windows 64-bit operating system. So, the steps below are to install python version
3.7.4 on Windows 7 device or to install Python 3. Download the Python Cheatsheet
here. The steps on how to install Python on Windows 10, 8 and 7 are divided into 4
parts to help understand better.

Download the Correct version into the system

Step 1: Go to the official site to download and install python using Google Chrome or
any other web browser. OR Click on the following link: https://www.python.org

Now, check for the latest and the correct version for your operating system.

Step 2: Click on the Download Tab.

Step 3: You can either select the Download Python for windows 3.7.4 button in
Yellow Color or you can scroll further down and click on download with respective to
their version. Here, we are downloading the most recent python version for windows
3.7.4

Step 4: Scroll down the page until you find the Files option.

Step 5: Here you see a different version of python along with the operating system.
 To download Windows 32-bit python, you can select any one from the three options:
Windows x86 embeddable zip file, Windows x86 executable installer or Windows
x86 web-based installer.

 To download Windows 64-bit python, you can select any one from the three options:
Windows x86-64 embeddable zip file, Windows x86-64 executable installer or
Windows x86-64 web-based installer.

Here we will install Windows x86-64 web-based installer. Here your first part
regarding which version of python is to be downloaded is completed. Now we move
ahead with the second part in installing python i.e., Installation

Note: To know the changes or updates that are made in the version you can click on
the Release Note Option.

Installation of Python

Step 1: Go to Download and Open the downloaded python version to carry out the
installation process.
Step 2: Before you click on Install Now, make sure to put a tick on Add Python 3.7 to
PATH.

Step 3: Click on Install NOW After the installation is successful. Click on Close.

With these above three steps on python installation, you have successfully and
correctly installed Python. Now is the time to verify the installation.

Note: The installation process might take a couple of minutes.

Verify the Python Installation

Step 1: Click on Start

Step 2: In the Windows Run Command, type “cmd”.

Step 3: Open the Command prompt option.

Step 4: Let us test whether the python is correctly installed. Type python –V and press
Enter.

Step 5: You will get the answer as 3.7.4

Note: If you have any of the earlier versions of Python already installed. You must
first uninstall the earlier version and then install the new one.

Check how the Python IDLE works

Step 1: Click on Start

Step 2: In the Windows Run command, type “python idle”.

Step 3: Click on IDLE (Python 3.7 64-bit) and launch the program

Step 4: To go ahead with working in IDLE you must first save the file. Click on File >
Click on Save

Step 5: Name the file and save as type should be Python files. Click on SAVE. Here I
have named the files as Hey World.

Step 6: Now for e.g., enter print (“Hey World”) and Press Enter.
You will see that the command given is launched. With this, we end our tutorial on
how to install Python. You have learned how to download python for windows into
your respective operating system.

Note: Unlike Java, Python does not need semicolons at the end of the statements
otherwise it won’t work.
 CHAPTER 7

SOURCE CODE

# Machine Learning-Based Differential Diagnosis of Erythemato-Squamous Diseases

from Clinical and Microscopic Features

# Importing Libraries

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.preprocessing import LabelEncoder

from imblearn.over_sampling import SMOTE

from sklearn.model_selection import train_test_split

from sklearn.tree import DecisionTreeClassifier

from sklearn.svm import SVC

import joblib

import os

from sklearn.metrics import precision_score

from sklearn.metrics import recall_score

from sklearn.metrics import f1_score

from sklearn.metrics import accuracy_score,confusion_matrix,classification_report

import warnings

warnings.filterwarnings('ignore')

# Importing Dataset
dataset=pd.read_csv("data.csv")

dataset['class'].unique()

dataset.head()

dataset.info()

dataset.isnull().sum()

dataset.replace('?', np.nan, inplace=True)

dataset.dropna(inplace =True)

dataset.describe()

# Create a count plot

sns.set(style="darkgrid") # Set the style of the plot

plt.figure(figsize=(8, 6)) # Set the figure size

# Replace 'dataset' with your actual DataFrame and 'Drug' with the column name

ax = sns.countplot(x='class', data=dataset, palette="Set3")

plt.title("Count Plot") # Add a title to the plot

plt.xlabel("Categories") # Add label to x-axis

plt.ylabel("Count") # Add label to y-axis

# Annotate each bar with its count value

for p in ax.patches:

ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),

ha='center', va='center', fontsize=10, color='black', xytext=(0, 5),

textcoords='offset points')

plt.xticks(rotation=90)

plt.show() # Display the plot

# Label Encoding

le= LabelEncoder()

dataset['class']=le.fit_transform(dataset['class'])

dataset

#Defining Dependent and independent variables

X=dataset.iloc[:,0:34]

y=dataset.iloc[:,-1]

#Applying SMOTE

smote = SMOTE(sampling_strategy='auto', random_state=42)

X,y= smote.fit_resample(X, y)

# Create a count plot

sns.set(style="darkgrid") # Set the style of the plot

plt.figure(figsize=(8, 6)) # Set the figure size

# Replace 'dataset' with your actual DataFrame and 'Drug' with the column name

ax = sns.countplot(x=y, data=dataset, palette="Set3")

plt.title("Count Plot") # Add a title to the plot

plt.xlabel("Categories") # Add label to x-axis

plt.ylabel("Count") # Add label to y-axis

# Annotate each bar with its count value

for p in ax.patches:

ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),

 ha='center', va='center', fontsize=10, color='black', xytext=(0, 5),

textcoords='offset points')

plt.show() # Display the plot

#Datasplitting

X_train,X_test,y_train,y_test= train_test_split(X,y,test_size=0.20)

X_train.shape

X_test.shape

X_train.shape

X_test.shape

#Building a ML Model

labels=['psoriasis','seboreic dermatitis','lichen planus','pityriasis rosea','cronic

dermatitis','pityriasis rubra pilaris' ]

#defining global variables to store accuracy and other metrics

precision = []

recall = []

fscore = []

accuracy = []

#function to calculate various metrics such as accuracy, precision etc

def calculateMetrics(algorithm, predict, testY):

testY = testY.astype('int')

predict = predict.astype('int')

p = precision_score(testY, predict,average='macro') * 100

r = recall_score(testY, predict,average='macro') * 100

 f = f1_score(testY, predict,average='macro') * 100

a = accuracy_score(testY,predict)*100

accuracy.append(a)

precision.append(p)

recall.append(r)

fscore.append(f)

print(algorithm+' Accuracy : '+str(a))

print(algorithm+' Precision : '+str(p))

print(algorithm+' Recall : '+str(r))

print(algorithm+' FSCORE : '+str(f))

report=classification_report(predict, testY,target_names=labels)

print('\n',algorithm+" classification report\n",report)

conf_matrix = confusion_matrix(testY, predict)

plt.figure(figsize =(5, 5))

ax = sns.heatmap(conf_matrix, xticklabels = labels, yticklabels = labels, annot =

True, cmap="Blues" ,fmt ="g");

ax.set_ylim([0,len(labels)])

plt.title(algorithm+" Confusion matrix")

plt.ylabel('True class')

plt.xlabel('Predicted class')

plt.show()

# DecisionTreesClassifier

# Check if the model files exist

if os.path.exists('Decision_trees_model.pkl'):
 # Load the trained model from the file

clf = joblib.load('Decision_trees_model.pkl')

print("Model loaded successfully.")

predict = clf.predict(X_test)

calculateMetrics("DecisionTreesClassifier", predict, y_test)

else:

# Train the model (assuming X_train and y_train are defined)

clf = DecisionTreeClassifier(max_depth=3)

clf.fit(X_train, y_train)

# Save the trained model to a file

joblib.dump(clf, 'Decision_trees_model.pkl')

print("Model saved successfuly.")

predict = clf.predict(X_test)

calculateMetrics("DecisionTreesClassifier", predict, y_test)

# Support Vector Machine (SVM) classifier

if os.path.exists('SVM_model.pkl'):

# Load the trained model from the file

clf = joblib.load('SVM_model.pkl')

print("Model loaded successfully.")

predict = clf.predict(X_test)

calculateMetrics("Support Vector Machine Classifier", predict, y_test)

else:

# Train the model (assuming X_train and y_train are defined)

clf = SVC(kernel='linear', random_state=42)

 clf.fit(X_train, y_train)

# Save the trained model to a file

joblib.dump(clf, 'SVM_model.pkl')

print("Model saved successfully.")

predict = clf.predict(X_test)

calculateMetrics("Support Vector Machineclassifier", predict, y_test)

#showing all algorithms performance values

columns = ["Algorithm Name","Precison","Recall","FScore","Accuracy"]

values = []

algorithm_names = ["Support Vector Machine Classifier", "DecisionTreesClassifier"]

for i in range(len(algorithm_names)):

values.append([algorithm_names[i],precision[i],recall[i],fscore[i],accuracy[i]])

temp = pd.DataFrame(values,columns=columns)

temp

# prediction

test=pd.read_csv("test.csv")

test

# Make predictions on the selected test data

predict = clf.predict(test)

# Loop through each prediction and print the corresponding row

for i, p in enumerate(predict):
 print(test.iloc[i]) # Print the row

print(f"Row {i}:**************************************************
{labels[p]}")

test['predict']=predict

test

test['Predicted'] = [labels[p] for p in predict]

test
 CHAPTER 8

RESULTS

8.1 Implementation Description:

The implementation of this research involves a series of steps aimed at building a

robust machine learning system for the differential diagnosis of erythemato-squamous
diseases using clinical and microscopic features. The process includes data
preprocessing, model training, and evaluation. Below is a detailed description of each
phase:

1. Data Import and Exploration:

o The dataset (data.csv) is loaded into a DataFrame using pandas. This dataset
contains various clinical and microscopic features associated with different
erythemato-squamous diseases.

o Initial exploration includes checking the unique values in the class column,
examining the dataset's structure, and identifying missing values.

2. Data Preprocessing:

o Handling Missing Values: Missing values, represented by '?', are replaced

with NaN. Rows with missing values are then removed to ensure the
dataset's integrity.

o Label Encoding: The categorical target variable class is converted into

numerical format using LabelEncoder. This step is crucial for machine
learning algorithms that require numerical input.

3. Data Balancing:

o SMOTE: The Synthetic Minority Over-sampling Technique (SMOTE) is

employed to address class imbalance by generating synthetic samples for
underrepresented classes. This balances the dataset and improves the
model’s ability to generalize.
4. Data Splitting:

o The dataset is split into training and testing subsets using an 80-20 ratio. This
separation allows the model to be trained on one part of the data and
evaluated on another to assess its performance.

5. Model Training:

o Decision Tree Classifier: The model is initially trained using the Decision
Tree Classifier with a maximum depth of 3. The trained model is saved to a
file using joblib and reloaded if available. Performance metrics such as
accuracy, precision, recall, and F1 score are computed.

o Support Vector Machine (SVM): The Support Vector Machine model is

then trained using a linear kernel. Similar to the Decision Tree model, the
SVM model is saved and reloaded as needed. Performance metrics are
evaluated to compare with the Decision Tree model.

6. Performance Evaluation:

o Metrics Calculation: For both models, performance metrics including

accuracy, precision, recall, and F1 score are calculated. The
classification_report and confusion_matrix are generated to provide insights
into the models' effectiveness.

o Visualization: Confusion matrices are visualized using seaborn to

understand the model's prediction errors and class-wise performance.

7. Prediction and Testing:

o A separate test dataset (test.csv) is used to make predictions using the trained
SVM model. The predictions are appended to the test dataset, and the results
are printed and displayed for review.

8. Advantages of the Proposed System:

o Improved Accuracy: The SVM model is expected to outperform the

Decision Tree Classifier, providing better classification accuracy and
handling complex patterns in the data.
 o Robust to Overfitting: SVM's ability to handle high-dimensional data and
avoid overfitting makes it a suitable choice for this application.

o Versatility: The SVM's flexibility with different kernels allows for effective
modeling of various data distributions.

The implementation effectively integrates data preprocessing, model training, and

evaluation phases to build a reliable machine learning-based system for diagnosing
erythemato-squamous diseases. The use of both Decision Tree Classifier and SVM
models ensures a comprehensive evaluation of classification performance, leading to
improved diagnostic accuracy and efficiency.

8.2 Dataset Description

The dataset provided contains clinical and microscopic features used for the
differential diagnosis of erythemato-squamous diseases. Each row represents a sample
with various attributes, including both clinical and histopathological characteristics.
The dataset is structured as follows:

Columns:

1. erythema: Degree of erythema observed in the lesion (e.g., 1: mild, 2: moderate, 3:

severe).

2. scaling: Presence and extent of scaling on the lesion (e.g., 0: absent, 1: mild, 2:
moderate, 3: severe).

3. definite_borders: Presence of well-defined borders of the lesion (e.g., 0: absent, 1:

present).

4. itching: Degree of itching associated with the lesion (e.g., 0: absent, 1: mild, 2:
moderate, 3: severe).

5. koebner_phenomenon: Presence of Koebner phenomenon (e.g., 0: absent, 1:

present).

6. polygonal_papules: Presence of polygonal papules (e.g., 0: absent, 1: present).

7. follicular_papules: Presence of follicular papules (e.g., 0: absent, 1: present).

8. oral_mucosal_involvement: Involvement of oral mucosa (e.g., 0: absent, 1:
present).

9. knee_and_elbow_involvement: Involvement of knees and elbows (e.g., 0: absent, 1:

present).

10. scalp_involvement: In volvement of the scalp (e.g., 0: absent, 1: present).

11. family_history: Family history of similar conditions (e.g., 0: absent, 1: present).

12. melanin_incontinence: Presence of melanin incontinence in histopathological

findings (e.g., 0: absent, 1: present).

13. eosinophils_in_the_infiltrate: Presence of eosinophils in the infiltrate (e.g., 0:

absent, 1: present).

14. PNL_infiltrate: Presence of polymorphonuclear leukocytes (PNL) in the infiltrate

(e.g., 0: absent, 1: present).

15. fibrosis_of_the_papillary_dermis: Presence of fibrosis in the papillary dermis (e.g.,

0: absent, 1: present).

16. exocytosis: Presence of exocytosis (e.g., 0: absent, 1: present).

17. acanthosis: Presence of acanthosis (e.g., 0: absent, 1: present).

18. hyperkeratosis: Presence of hyperkeratosis (e.g., 0: absent, 1: present).

19. parakeratosis: Presence of parakeratosis (e.g., 0: absent, 1: present).

20. clubbing_of_the_rete_ridges: Clubbing of the rete ridges (e.g., 0: absent, 1:

present).

21. elongation_of_the_rete_ridges: Elongation of the rete ridges (e.g., 0: absent, 1:

present).

22. thinning_of_the_suprapapillary_epidermis: Thinning of the suprapapillary

epidermis (e.g., 0: absent, 1: present).

23. spongiform_pustule: Presence of spongiform pustules (e.g., 0: absent, 1: present).

24. munro_microabcess: Presence of Munro microabscesses (e.g., 0: absent, 1:

present).
25. focal_hypergranulosis: Presence of focal hypergranulosis (e.g., 0: absent, 1:
present).

26. disappearance_of_the_granular_layer: Disappearance of the granular layer (e.g.,

0: absent, 1: present).

27. vacuolisation_and_damage_of_basal_layer: Presence of vacuolisation and

damage to the basal layer (e.g., 0: absent, 1: present).

28. spongiosis: Presence of spongiosis (e.g., 0: absent, 1: present).

29. saw_tooth_appearance_of_retes: Saw-tooth appearance of the rete ridges (e.g., 0:

absent, 1: present).

30. follicular_horn_plug: Presence of follicular horn plugs (e.g., 0: absent, 1: present).

31. perifollicular_parakeratosis: Presence of perifollicular parakeratosis (e.g., 0:

absent, 1: present).

32. inflammatory_monoluclear_infiltrate: Presence of inflammatory mononuclear

infiltrate (e.g., 0: absent, 1: present).

33. band_like_infiltrate: Presence of band-like infiltrate (e.g., 0: absent, 1: present).

34. age: Age of the patient (numerical value).

35. class: Disease classification (e.g., psoriasis, lichen planus, seborrheic dermatitis,
chronic dermatitis, pityriasis rosea, pityriasis rubra pilaris).

8.3 Results Description:

 Figure 1: First 5 rows of the dataset.

Figure 1 displays the initial five rows of the dataset, showcasing a snapshot of the raw
data used for the study. Each row represents a patient case with clinical and
microscopic features, and the columns include attributes such as age, family history,
and various microscopic findings. The 'class' column indicates the disease type, with
values like psoriasis and seboreic dermatitis. This figure provides an overview of the
structure and format of the data before any preprocessing steps, allowing us to
understand the types of features involved in diagnosing erythemato-squamous
diseases.
 Figure 2: Count plot of the various types of the class column of the dataset before
applying smote.

Figure 2 illustrates the distribution of different classes within the dataset before
applying Synthetic Minority Over-sampling Technique (SMOTE). This count plot
highlights the class imbalance, with certain diseases like psoriasis having significantly
more samples compared to others like pityriasis rubra pilaris. The imbalance can
negatively impact model performance by biasing towards the majority class. This
visualization underscores the need for techniques like SMOTE to balance the classes
and ensure that the model learns effectively from all categories.
Figure 3: Count plot of the various types of classes of the class column of the dataset
after applying smote.

Figure 3 depicts the class distribution after applying SMOTE, a technique used to
generate synthetic samples for minority classes to achieve a balanced dataset. Post-
SMOTE, each class has an equal number of samples, as reflected in the uniform
heights of the bars in the count plot. This balance is crucial for training the machine
learning models as it prevents bias towards any particular class, ensuring that the
model has sufficient examples to learn from each disease category. This figure
confirms the successful application of SMOTE to address class imbalance issues.
 Figure 4: Confusion matrix of Decision Tree Algorithm to the dataset.

Figure 4 presents the confusion matrix of the Decision Tree algorithm's performance
on the test dataset. The matrix provides a detailed breakdown of the model's
predictions against the actual labels, showing true positives, true negatives, false
positives, and false negatives for each class. Each cell in the matrix represents the
count of predictions made by the model for a given class. High values along the
diagonal indicate good predictive accuracy for those classes. This figure helps in
understanding the Decision Tree model's strengths and weaknesses in classifying the
various types of erythemato-squamous diseases.

Figure 5: Confusion matrix of Support vector classifier Algorithm to the dataset.

Figure 5 shows the confusion matrix for the Support Vector Classifier (SVC) applied
to the test dataset. Similar to Figure 4, this matrix breaks down the classifier's
performance by comparing predicted and actual labels. The SVC confusion matrix
highlights how well the model distinguishes between different disease types. High
values on the diagonal suggest accurate predictions, while off-diagonal values
indicate misclassifications. This figure is critical for assessing the SVC's efficacy and
areas where it may struggle, providing insights for further model tuning and
improvement.
 Figure 6: Presents the Model Predication on test dataset.

Figure 6 presents the results of the model predictions on the test dataset. This figure
includes a table showing each test sample with its corresponding predicted disease
class. The predictions are compared against the actual class labels to evaluate the
model's performance. This comprehensive view of model output allows for detailed
examination of prediction accuracy, highlighting instances of correct classifications
and misclassifications. It provides a practical perspective on how the trained model
would perform in real-world diagnostic scenarios, validating its applicability and
reliability in clinical settings.

Table 1: Performance Metrics of SVM and Decision Tree Classifier Models.

 Table 1 provides a comparative analysis of the performance metrics for two machine
learning algorithms: the Support Vector Machine (SVM) Classifier and the Decision
Tree Classifier, applied to the task of diagnosing erythemato-squamous diseases. The
table lists four key evaluation metrics for each model: Precision, Recall, F-Score, and
Accuracy.

 Algorithm Name: This column indicates the name of the machine learning
algorithm evaluated.

 Precision: Precision measures the accuracy of the positive predictions made by the
model. The Support Vector Machine Classifier achieved a precision of 57.31%,
while the Decision Tree Classifier achieved a significantly higher precision of
99.07%.

 Recall: Recall indicates the ability of the model to correctly identify all relevant
instances. The SVM Classifier had a recall of 66.67%, whereas the Decision Tree
Classifier demonstrated superior recall with a score of 99.33%.

 F-Score: The F-Score is the harmonic mean of precision and recall, providing a
single metric that balances both. The SVM Classifier's F-Score was 60.16%, while
the Decision Tree Classifier had an outstanding F-Score of 99.18%.

 Accuracy: Accuracy measures the overall correctness of the model's predictions.

The SVM Classifier achieved an accuracy of 76.12%, compared to the Decision
Tree Classifier's near-perfect accuracy of 99.25%.

The table highlights the stark contrast between the two models, with the Decision
Tree Classifier significantly outperforming the SVM Classifier across all metrics.
This suggests that the Decision Tree model is highly effective for this particular
diagnostic task, providing accurate and reliable classifications for erythemato-
squamous diseases. The superior performance of the Decision Tree Classifier could
be attributed to its ability to handle the complex interactions among the clinical and
microscopic features of the dataset, making it a preferred choice for this application.
 CHAPTER 9

CONCLUSION AND FUTURE SCOPE

9.1 Conclusion

The study titled "Impact of Machine Learning-Based Differential Diagnosis of

Erythemato-Squamous Diseases" demonstrates the efficacy of machine learning
algorithms in accurately diagnosing complex dermatological conditions based on
clinical and microscopic features. By applying advanced classification techniques,
namely the Support Vector Machine (SVM) Classifier and the Decision Tree
Classifier, we achieved significant insights into their performance in a diagnostic
context.

The comparative analysis revealed that the Decision Tree Classifier significantly
outperformed the SVM Classifier in terms of precision, recall, F-score, and accuracy.
Specifically, the Decision Tree model achieved a near-perfect accuracy of 99.25%,
highlighting its capability to handle the intricate patterns within the dataset effectively.
This superior performance underscores the potential of decision tree algorithms in
medical diagnostics, where precision and recall are critical for patient outcomes.

Moreover, the study underscores the importance of data preprocessing steps, such as
handling missing values and applying SMOTE for class balancing, which are crucial
in enhancing the performance of machine learning models. The results demonstrated
that a well-preprocessed dataset, coupled with a robust algorithm, can provide reliable
and accurate diagnostic predictions.

In conclusion, this study validates the applicability of machine learning models,

particularly the Decision Tree Classifier, in the differential diagnosis of erythemato-
squamous diseases. The findings suggest that machine learning can be a powerful tool
in dermatology, assisting clinicians in making more accurate diagnoses and improving
patient care outcomes.

9.2 Future Scope

The promising results of this study open several avenues for future research and
development in the field of machine learning-based medical diagnostics. Firstly, there
is potential for extending the dataset to include more diverse and comprehensive
clinical and microscopic features, which could further enhance the model's accuracy
and generalizability across different populations and subtypes of erythemato-
squamous diseases.

Secondly, integrating additional machine learning algorithms, such as ensemble

methods (e.g., Random Forests, Gradient Boosting), could be explored to compare
their performance against the Decision Tree Classifier. These methods might offer
even greater accuracy and robustness by leveraging the strengths of multiple models.

Another important future direction involves the incorporation of deep learning

techniques, particularly Convolutional Neural Networks (CNNs), which have shown
exceptional performance in image-based diagnostics. Applying CNNs to microscopic
images of skin lesions could potentially yield more nuanced and precise diagnostic
insights.

Moreover, developing an integrated decision support system that combines machine

learning predictions with clinical decision-making processes can enhance its practical
applicability in healthcare settings. Such a system could provide real-time diagnostic
support to dermatologists, improving the efficiency and accuracy of diagnosis.

Lastly, conducting longitudinal studies to assess the impact of machine learning-based

diagnostics on patient outcomes, treatment plans, and healthcare costs would be
invaluable. This would provide a comprehensive understanding of the real-world
benefits and limitations of implementing these technologies in clinical practice.

In summary, the future scope of this research is vast and multifaceted, with numerous
opportunities to advance the field of dermatological diagnostics through innovative
machine learning applications. By continuing to refine these models and expanding
their capabilities, we can move closer to achieving more accurate, efficient, and
personalized healthcare solutions.
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