Easy Diagnose - Heart Disease Prediction

Overview

Easy Diagnose is a web application built for predicting heart disease based on user input parameters. It provides a user-friendly interface developed using Streamlit, allowing users to sign up, log in, and receive heart disease predictions based on their input.

Features

• User Authentication: Users can sign up or log in to access the prediction functionality.
• Input Parameters: Users can enter relevant medical parameters for heart disease prediction.
• Prediction Results: The application provides a prediction result based on the input parameters.
• Model: The prediction model is trained using the Random Forest method on the Heart Disease dataset from UCI Machine Learning Repository.

Installation

1. Clone the repository:

   git clone https://github.com/your-username/easy-diagnose.git

2. Navigate to the project directory:

   cd easy-diagnose

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Start the Streamlit server:

   streamlit run app.py

2. Access the application in your web browser using the provided URL (typically http://localhost:8501).

Data Source

The heart disease prediction model is trained using the Heart Disease dataset from the UCI Machine Learning Repository. The data preprocessing and model training details are included in the codebase.

Contributing

We welcome contributions to improve Easy Diagnose. Feel free to submit issues or pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.