MULTI DISEASE PREDICTION MODEL

USING MACHINE LEARNING

Batch ID:- MIP-IT-30
Under the Guidance
of
G. Manoj Kumar
Assistant Professor Of CSE

Presented by
M. Sanjay Kumar (197Y1A1243)
E. Swarna Latha (197Y1A1255)
 CONTENTS
• Abstract
• Introduction
• Objective
• Literature Survey
• Existing Solution
• Proposed Solution
• Basic Concepts
• Machine Learning and Deep Learning algorithms
• Hardware and Software Requirements
• Data Collection
• Architecture
• Conclusion
ABSTRACT

The purpose of our project is to predict multiple diseases in one common

system. Many of the existing systems for health care analysis are concentrating on
one disease per analysis .But our system mainly focuses on multiple disease
prediction. We can predict multiple disease using flask API .To implement multiple
disease analysis we make use of machine learning algorithms , TensorFlow and
flask API .Python pickling is used to save the model behaviour and python
unpickling is used to load the pickle file whenever required .In multiple disease
analysis we basically analyse all the parameters which cause the disease .Later on
we also test whether these parameters are involved in other diseases .The
importance of this analysis is to analyse the maximum diseases ,so that to monitor
the patient’s condition and warn patients in advance to decrease mortality ratio.
INTRODUCTION
Human life is evolving every single day, Life is full of uncertainty. Every now and then we come across many people suffering
from fatal health issues due to late identification of diseases. The study says, One in two Indian diabetics are unaware of their
condition. Nearly 463 million people in the world have diabetes. One in four deaths in India are now because of CVDs with ischemic
heart disease and stroke responsible for more than 80% of this burden. The study estimates more than 50 million people in the world,
considering the adult population, would be affected with chronic liver disease. But, it can be prevented by identifying the disease in its
early stage. The project “Disease Prediction using Machine Learning” is developed to identify general disease in earlier stages.
Now-a-days, people put health as a secondary priority, which leads to various problems. According to research, 40% of people ignore
the symptoms, due to fear of facing financial issues or other generic reasons. Many cannot afford to consult a doctor or some are very
busy and have a tight schedule, but ignoring the recurring symptoms for a long period of time may have severe consequences to their
health. According to research 70% of people in India suffer from common diseases and the mortality rate is 25%, mostly due to
ignorance in early stages. The main motive to develop this project is that a user can conveniently have a check-up of their health, if
they have any of the symptoms. Due to an increased amount of data growth in the medical and healthcare field the accurate analysis
on medical data which has been benefited from early patient care. With the help of disease data, data mining finds hidden pattern
information in the huge medical data of the data set. We proposed a disease prediction platform, based on the vitals of the patient. Our
Disease web application predicts the occurrence of heart disease, diabetes, breast cancer, pneumonia & malaria. We have also
provided a proper diet plan according to the diseases. Along with it, we have also provided an about page which gives information
about the symptoms & information about the diseases.
OBJECTIVE

• To predict the likelihood of contracting the disease.

• To give information about the disease that are predicted.
• To provide the diet and exercise information.
• To provide no expense disease diagnosis.
EXISTING SYSTEM

Many of existing analysis involved analysing particular disease. When a

user wants to analyse diabetes needs to use one analysis and same
user wants to analyse heart disease then user has to use one more
model. This is a time taking process. And also if any user having more
than one disease but in existing system if it is able to predict only one
disease then there is a chance of mortality rate increase due to not able
to predict the other disease in advance.
PROPOSED SYSTEM

In multi disease model prediction, it is possible to predict more

than one disease at a time. So user no need to traverse many
models to predict the diseases. It will reduce time and also due to
predicting multiple diseases at a time there is a chance of
reducing mortality rate. In this we are going to compare different
algorithms to predict the particular disease so that we obtain higher
accuracy for that particular disease. And we are going to give some
preventive measures so that disease may cured in early stage.
BASIC CONCEPTS

• Machine learning (ML) is a type of artificial intelligence (AI) that allows

software applications to become more accurate at predicting outcomes
without being explicitly programmed to do so.
• TensorFlow is an open source framework developed by Google researchers to run
machine learning, deep learning and other statistical and predictive analytics
workloads.
• Flask is basically a micro web application framework written in Python.
Developers often use Flask for making web applications, HTTP request
management, and template rendering. By “micro web application,” we mean that
it is not a full-stack framework.
 Machine Learning and Deep Learning Algorithms
• Logistic Regression- It is used to calculate or predict the probability of a binary (yes/no) event occurring.
• SVM- Support Vector Machines (SVM) works best on linearly separable data, i.e. data that can be separated into two distinct
classes using a straight line or hyperplane.
• KNN- k-nearest neighbors algorithm uses proximity to make classifications or predictions about the grouping of an individual
data point.
• Naive Bayes- It performs well in cases of categorical input variables compared to numerical variables.
• Random Forest- It combines multiple decision trees to create a “forest”.
• Gradient Boosting- It gives a prediction model in the form of an ensemble of weak prediction models, which are
typically decision trees.
• Stochastic Gradient Descent- It is an optimization algorithm used to find the model parameters that correspond to the best fit
between predicted and actual outputs
• Convolutional Neural Network- CNN is a type of artificial neural network, which is widely used for image/object
recognition and classification.
• Visual Geometry Group 16(VGG16)- VGG16 is object detection and classification algorithm which is able to classify 1000
images of 1000 different categories with 92.7% accuracy. It has 16 layers of network.
HARDWARE AND SOFTWARE REQUIREMENTS

• Hardware: Windows 10,4GB RAM

• Software: Python ,Machine Learning and Deep Learning Algorithms,

Flask API, TensorFlow.
DATA COLLECTION

Heart Dataset
• For predicting the occurrence of heart disease we have used the “Heart Disease Dataset” by UCI from Kaggle.
This dataset consists of 13 medical predictor features and one target feature. The attributes are as follows
chol, cp, trestbps(resting blood pressure),age,fbs(fasting blood pressure), sex, restecg, exang, slope, thal, ca,
oldpeak, thalach. The dataset contains 303 instances and 75 attributes.
Diabetics Dataset
• For predicting the occurrence of Diabetes diseases we have used the “Pima Indians Diabetes Dataset” by
Kaggle. This dataset consists 8 medical predictor features and one target feature. The attributes are as
follows blood pressure, Pregnancies, Glucose, Skin Thickness, BMI, Insulin, Age and DiabetesPredigree.
Breast Cancer Dataset
• For predicting the occurrence of breast cancer diseases we have used the “Breast Cancer Wisconsin Data
Set” by Kaggle. This dataset contains 31 medical predictor features and one target feature. Some of the
important attributes are as follows diagnosis, id, radius-mean, texture-mean, perimeter-mean, area-mean,
smoothness-mean, compact-mean, concavity-mean, and concavity points-mean.
DATA COLLECTION

Diabetes dataset Heart Dataset

ARCHITECTURE
CONCLUSION

• As we move towards a digital world, with the help of AI, there can be early
diagnosis of the patient.
• Boon to common people and sometimes doctors.
• Saves time required for diagnosis the problem.
THANK YOU!