Disease Prediction

Welcome to the Disease Prediction repository! This project aims to leverage machine learning techniques to predict the likelihood of various diseases based on user input data. The goal is to provide an accessible tool for early diagnosis and preventive healthcare.

Introduction
Features
Technologies Used
Installation
Usage
Dataset
Model Training
Contributing
License
Contact

Introduction

The Disease Prediction project utilizes a machine learning model trained on medical data to predict the probability of a user having certain diseases. This can be particularly useful in areas with limited access to healthcare professionals, enabling users to get an early indication of potential health issues.

Features

User-friendly interface for inputting health data Predicts the likelihood of multiple diseases Provides a detailed report of the prediction results Scalable and easy to deploy

Technologies Used

JavaScript: Main programming language
React.js: Frontend library
Node.js: Backend runtime environment
Express.js: Web framework for the backend
MongoDB: Database for storing user data
TensorFlow.js: Machine learning library for training and inference

Installation

To get a local copy up and running, follow these steps:

Clone the repository:

git clone https://github.com/Ayu-hack/Disease-Prediction.git 
cd Disease-Prediction

Install the required packages for both the backend and frontend:

cd backend
npm install
cd ../frontend
npm install

Set up environment variables: Create a .env file in the backend directory and add the following:

env
MONGODB_URI= //your_mongodb_connection_string
PORT=5000

Start the frontend server:

cd frontend
npm run dev

Start the backend server:

cd backend
npm start

Usage

Open your web browser and go to http://localhost:3000.
Enter the required health data into the form.
Submit the form to get the prediction results.
Review the detailed report provided by the application.

Dataset

The dataset used for training the model is not included in the repository due to size and privacy constraints. However, you can use publicly available medical datasets or your own data to train the model. Ensure the data is preprocessed and cleaned before training.

Model Training

To train the model:

Ensure your dataset is in CSV format and located in the backend/data/ directory.
Modify the trainModel.js script to load your dataset.

Run the training script:

cd backend
node trainModel.js
The trained model will be saved to the backend/models/ directory.

Contributing

Contributions are welcome! Please follow these steps to contribute:

Fork the repository.
Create a new branch (git checkout -b feature/YourFeature).
Commit your changes (git commit -m 'Add some feature').
Push to the branch (git push origin feature/YourFeature).
Open a pull request.

Contact

For questions or suggestions, please contact:

Name: Ayu-hack Email: [email protected]

Thank you for visiting our repository! We hope our project aids in advancing healthcare accessibility and early disease detection.

thedarknight01 / disease-prediction Goto Github PK