Mini-Project

As part of our MINI Project, our team created a disease prediction website which predicts 6 diseases as per the information entered or provided by the user

Presentation : https://drive.google.com/file/d/1OmMfwq-APNHQKwyEkk06l9PbLyEVzHT0/view?usp=sharing

Table of Contents

• Table of Contents
• Introduction
• Tech Stack
• System Architecture
• Contributors
• Institute

Introduction

Our web app aims to predict whether the user has a certain disease or not. The prediction will be done on the basis of general questions asked in the form of multiple-choice questions. Some models will also be based on the X-ray and scan images provided by the users. The user can choose among 6 diseases and on choosing a certain disease general information about that disease and informative facts are provided to the user.

Tech Stack

• Front End: HTML, CSS, Bootstrap, JavaScript
• Back End: Flask
• ML models: Python libraries like TensorFlow, random forest, logistic regression

Our ML MODELS

1. Lung Cancer : XGBoost 93%
2. Diabetes : Random Forest 80%
3. Heart Diseases : Random Forest 88%
4. Urinary Inflammation : Logistic Regression 100%
5. Alzheimer's : InceptionV3 and Transfer learning 79%
6. Tuberculosis : InceptionV3 and Transfer learning 94%

Average Accuracy : 89-90%

System Architecture

Contributors

Sr No.	Name	E-mail	Roll No.
1.	Varshaah Karkala	varshaah.k@somaiya.edu	16010120193
2.	Shruti Tyagi	shruti.tyagi@somaiya.edu	16010120202
3.	Ridhiman Dhariwal	ridhiman.dhariwal@somaiya.edu	16010120190
4.	Pyanshi Jain	pyanshi.jain@somaiya.edu	16010120201

Institute