Revive - A Machine Learnig model

Machine Learning Based Web Application for multiple Disease Prediction

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Project Overview

Revive is a Machine Learning powered web application that predicts multiple diseases using patient medical data.

The system uses trained Machine Learning and Deep Learning models integrated with a Flask web application.

Users can enter medical parameters and the system will predict possible diseases based on trained datasets.

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Features

✔ Multiple Disease Prediction
✔ Machine Learning Models
✔ Deep Learning Models
✔ User Friendly Web Interface
✔ Fast Prediction System
✔ Accurate Results

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Table of Contents

• Problem Statement
• Why This Project
• Flow Chart
• Directory Structure
• Quick Start
• Screenshots
• Technical Details
• Developer
• License

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Problem Statement

Revive can be time consuming and sometimes prone to human errors.

The objective of this project is to build a Machine Learning based medical diagnosis system that helps in predicting diseases early.

The system allows users to input medical data which is then processed by trained models to predict possible diseases.

This system helps:

• Doctors
• Medical Researchers
• Patients

to quickly identify possible diseases using AI.

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Why This Project

Humans can make mistakes during diagnosis due to fatigue or workload.

Machine Learning systems analyze large datasets and identify patterns which help in improving prediction accuracy.

Advantages of this project:

• Early disease detection
• Reduced diagnostic errors
• Faster analysis
• Multiple disease predictions

Datasets were collected from Kaggle and UCI Machine Learning Repository.

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Flow Chart

Start │ ▼ User Opens Website │ ▼ Flask Server Starts (app.py) │ ▼ Homepage Loads (index.html) │ ▼ User Selects Disease Prediction (Malaria / Pneumonia / Kidney / Liver) │ ▼ User Uploads Image / Enters Data │ ▼ Flask Receives Input (POST Request) │ ▼ Load Trained ML Model (.pkl) │ ▼ Preprocess Input Data │ ▼ Model Prediction │ ▼ Prediction Result Generated │ ▼ Result Sent to HTML Template │ ▼ Result Displayed to User │ ▼ End

Visual FC

Quick Start

Step 1

Clone the repository

git clone https://github.com/codexshami/Revive.git

Quick Start

Step 1

Clone the repository

git clone https://github.com/codexshami/Revive

Step 2

Go to project directory

cd Revive

Step 3

Install dependencies

pip install -r requirements.txt

Step 4

Run the application

python app.py

or

flask run

Step 5

Open browser

http://127.0.0.1:5000

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Screenshots

Home

Diabetes Prediction

Breast Cancer Prediction

Heart Disease Prediction

Kidney Disease Prediction

Liver Disease Prediction

Malaria Detection

Pneumonia Detection

Services

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Technical Details

This web application was developed using Flask Web Framework.

Machine Learning models were trained on large datasets and integrated into the web application.

The system can predict the following diseases:

• Diabetes
• Breast Cancer
• Heart Disease
• Kidney Disease
• Liver Disease
• Malaria
• Pneumonia

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Model Accuracy

Disease	Model Type	Accuracy
Diabetes	Machine Learning	98.25%
Breast Cancer	Machine Learning	98.25%
Heart Disease	Machine Learning	85.25%
Kidney Disease	Machine Learning	99%
Liver Disease	Machine Learning	78%
Malaria	CNN Deep Learning	96%
Pneumonia	CNN Deep Learning	95%

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Developer

Mohd Shami

LinkedIn
https://www.linkedin.com/in/mohd-shami-792133276

GitHub
https://github.com/codexshami

Email
codexshami@gmail.com

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License

Licensed under the Apache License 2.0

Copyright 2026 Mohd Shami

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND.

Made by Mohd Shami