NAME : V VEERARAGAVAN
REG.NO: 814722103035
DEPT : CIVIL
COLLEGE : SRM TRP ENGINEERING
COLLEGE
AI POWERED MEDICAL DIAGNOSIS (FOCUSING ON A
SPECIFIC DISEASE)
PHASE 3: COMMUNICATION AND FUTURE
EXPLORATION
ABSTRACT
This project focuses on harnessing the power of artificial intelligence (AI) to
revolutionize the diagnosis and management of [specific disease]. By
leveraging cutting-edge AI algorithms, we aim to create a sophisticated
diagnostic tool tailored specifically for [specific disease], offering
unprecedented accuracy and efficiency.The core of our project lies in the
development of a robust AI model trained on diverse datasets comprising
medical records, imaging scans, laboratory results, and other relevant
patient information. Through machine learning techniques, the AI model
learns to recognize subtle patterns and correlations that are indicative of
[specific disease], enabling early detection and precise diagnosis.
into Our project is about translating complex medical data
actionable insights for healthcare professionals. We envision a user-friendly
interface that allows doctors to input patient data seamlessly and receive instant
diagnostic recommendations. This interface will serve as a bridge between AI
technology and clinical practice, empowering healthcare providers with timely
information to make informed decisions and improve patient outcomes.
In conclusion, our project represents a significant step forward in
the application of AI to healthcare, with [specific disease] as our focal point.
Through collaboration, innovation, and a commitment to ethical principles, we
aim to empower healthcare providers with advanced diagnostic tools that will
transform the way we diagnose and treat [specific disease]. Together, we can
build a healthier future for all.
Artificial intelligence in disease diagnosis:
Detecting any irresistible ailment is nearly an afterward movement and
forestalling its spread requires ongoing data and examination. Hence,
acting rapidly with accurate data tosses a significant effect on the lives
of individuals around the globe socially and financially. The best thing
about applying AI in health care is to improve from gathering and
processing valuable data to programming surgeon robots. This section
expounds on the various techniques and applications of artificial
intelligence, disease symptoms, diagnostics issues, and a framework for
disease detection modelling using learning models and AI in healthcare
applications.
Framework for AI in disease detection modelling
AI describes the capability of a machine to study the way a human learns,
e.g., through image identification and detecting pattern in a problematic
situation. AI in health care alters how information gets composed, analysed, and
developed for patient care.
System planning is the fundamental abstract design of the system. It includes
the framework’s views, the course of action of the framework, and how the
framework carries on underneath clear conditions. A solid grip of the
framework design can help the client realize the limits and boundaries of the
said framework.. In pre-preparing, real-world information requires upkeep and
pre-preparing before being taken care of by the calculation. Because of the
justifiable explanation, real-world data regularly contains mistakes regarding the
utilized measures yet cannot practice such blunders. Accordingly, information
pre-preparing takes this crude information, cycles it, eliminates errors, and spares
it an extra examination. Information experiences a progression of steps during
pre-handling. Information is purged by various strategies in information
cleaning. These strategies involve gathering information, such as filling the
information spaces that are left clear or decreasing information, such as the
disposal of commas or other obscure characters. In information osmosis, the
information is joined from a combination of sources. The information is then
amended for any blend of mistakes, and they are quickly taken care of.
Information Alteration: Data in this progression is standardized, which
depends upon the given calculation. Information standardization can
be executed utilizing several ways. This progression is obligatory in most
information mining calculations, as the information wants to be as perfect
as possible. Information is then mutual and developed. Information
Lessening: This progression in the strategy centers to diminish the
information to more helpful levels.
Informational collection and test information: The informational
collection is segregated into parts preparing and testing informational indexes.
The preparation information is utilized to gauge the actual examples of the data.
Equivalent to information needed for preparing and testing, experimental data is
often replicated from a similar informational index. After the model has been
pre-handled, the jiffy step is to test the accuracy of the framework.
Systematic model: Analytical displaying strategies are utilized to
calculate the probability of a given occurrence function given commitment
factors, and it is very productive in illness expectation. It tends to imagine what
the individual is experiencing in light of their info indications and prior
determinations.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS
1. High-performance computing hardware (e.g., multi-core CPU, GPU, or
specialized AI accelerators like TPUs) for training and inference tasks.
2. RAM-4 GB or higher
SOFTWARE REQUIREMENTS
1. 2. Operating System- Windows, Linux, or macOS.
Development Environment- TensorFlow, PyTorch, or Keras.
TOOLS AND VERSIONS:
1. TensorFlow- Version: 2.7.0
2. Docker- Version: 20.10.11
3. Flask-Version: 2.0.2
4. Scikit-learn-Version : 0.24.2
FLOWCHART:
CODE IMPLEMENTATION(SAMPLE CODE):
# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split from
sklearn.preprocessing import StandardScaler from
sklearn.ensemble import RandomForestClassifierfrom
sklearn.metrics import accuracy_score
# Load the dataset
data = pd.read_csv('diabetes_dataset.csv')
# Perform data preprocessing
# Handle missing values, outliers, and inconsistencies#
Feature engineering
# Normalize the data
# Split the dataset into train and test sets
X = data.drop('target_variable', axis=1) # Featuresy=
data['target_variable'] # Target variable
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
# Standardize features by removing the mean and scaling to unit variancescaler =
StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Train the model
model = RandomForestClassifier()
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Deployment (Flask example)
from flask import Flask, request, jsonify
app = Flask( name )
@app.route('/predict', methods=['POST'])
def predict():
data = request.get_json()
# Perform necessary data preprocessing on input data#
Make prediction using the trained model
prediction = model.predict(data)
return jsonify({'prediction': prediction.tolist()})
if name == ' main ':
app.run(debug=True)
OUTPUT(SCREEN SHOTS):
CONCLUSION AND FUTURE SCOPE:
When it comes to disease diagnosis, accuracy is critical for planning,
effective treatment and ensuring the well-being of patients. AI is a vast and
diverse realm of data, algorithms, analytics, deep learning, neural networks,
and insights that is constantly expanding and adapting to the needs of the
healthcare industry and its patients. According to the findings of this study,
AI approaches in the healthcare system, particularly for illness detection,
are essential. Aiming at illuminating how machine and deep learning
techniques work in various disease diagnosis areas, the current study has
been divided into several sections that cover the diagnosis of alzheimer’s,
cancer, diabetes, chronic diseases, heart disease, stroke and
cerebrovascular disease, hypertension, skin disease, and liver disease.
The introduction and contribution were covered in the first section,
followed by an evaluation of the quality of the work and an examination of AI
approaches and applications. Later, various illness symptoms and diagnostic
difficulties, a paradigm for AI in disease detection models, and various AI
applications in healthcare were discussed.
The reported work on multiple diseases and the comparative analysis of
different techniques with the used dataset as well as the results of an applied
machine and deep learning methods in terms of multiple parameters such as
accuracy, sensitivity, specificity, an area under the curve, and F- score has also
been portrayed. Finally, the work that assisted researchers in determining the
most effective method for detecting illnesses is finished, as in future scope. In a
nutshell, medical experts better understand how AI may be used for illness
diagnosis, leading to more appropriate proposals for the future development of
AI based techniques
Contrary to considerable advancements over the past several years, the
area of accurate clinical diagnostics faces numerous obstacles that must
be resolved and improved constantly to treat emerging illnesses and
diseases effectively. Even healthcare professionals recognize the barriers
that must be overcome before sickness may be detected in conjunction
with artificial intelligence. Even doctors do not entirely rely on AI-based
approaches at this time since they are unclear of their ability to anticipate
illnesses and associated symptoms.
Thus much work is required to train the AI-based systems so that there will
be an increase in the accuracy to predict the methods for diagnosing diseases.
Hence, in the future, AI-based research should be conducted by keeping the
flaw mentioned earlier in consideration to provide a mutually beneficial
relationship between AI and clinicians. In addition to this, a decentralized
federated learning model should also be applied to create a single training
model for disease datasets at remote places for the early diagnosis of diseases.
