Heart Disease Risk Predictor

[](LICENSE) [](https://www.python.org/downloads/) [](https://streamlit.io/) [](https://xgboost.readthedocs.io/)

Overview

An advanced machine learning application that predicts heart disease risk using XGBoost. Built with Streamlit and trained on over 300,000 US health records, achieving 91.5% prediction accuracy.

Features

• Real-time Prediction: Instant heart disease risk assessment
• Advanced Analytics: Comprehensive health evaluation using 17 key parameters
• Smart Visualization: Color-coded risk zones with detailed explanations
• Health Insights: Personalized recommendations based on risk levels
• Robust Architecture: Error handling and input validation
• Responsive Design: Works seamlessly on desktop and mobile
• Extensible Platform: Ready for additional disease predictions

Requirements

Software Dependencies

Python >= 3.8
streamlit >= 1.10.0
xgboost >= 1.5.0
pandas >= 1.3.0
numpy >= 1.21.0

System Requirements

• RAM: 4GB minimum
• Storage: 500MB free space
• Processor: 2+ cores recommended

Quick Start

1. Clone the Repository

   git clone https://github.com/jvc-Byte/heart-disease-predictor.git
   cd heart-disease-predictor
   

2. Environment Setup

   python -m venv venv
   source venv/bin/activate  # Windows: venv\Scripts\activate
   pip install -r requirements.txt
   

3. Launch Application

   streamlit run app.py
   

4. Open browser at http://localhost:8501

Usage Guide

Input Parameters

Demographics

• Race
• Sex
• Age category

Health Metrics

• BMI category
• Sleep patterns
• Physical activity
• Health status
• Mobility assessment

Lifestyle

• Smoking history
• Alcohol consumption
• Exercise habits

Medical History

• Diabetes status
• Stroke history
• Chronic conditions

Risk Zones

Zone	Range	Action Required
Green	0-25%	Maintain current health practices
Yellow	25-50%	Consider lifestyle improvements
Orange	50-75%	Consult healthcare provider
Red	75-100%	Seek medical attention

Project Structure

heart-disease-predictor/
│
├── app.py                 # Main application file
├── requirements.txt       # Project dependencies
├── models/               
│   └── xgboost_model.pkl # Trained model
├── assets/
│   └── images/           # UI resources
├── tests/                # Unit tests
└── docs/                 # Documentation

Model Information

• Architecture: XGBoost (Gradient Boosting)
• Training Data: 300,000+ US health records (2020)
• Accuracy: 91.5%
• Features: 17 health parameters
• Validation: K-fold cross-validation
• Updates: Quarterly retraining

Development

Setup Development Environment

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
python -m pytest

# Run linter
flake8 .

Contributing Guidelines

1. Fork the repository
2. Create feature branch (git checkout -b feature/Enhancement)
3. Commit changes (git commit -m 'Add Enhancement')
4. Push to branch (git push origin feature/Enhancement)
5. Open Pull Request

Author

jvc-Byte

• Email: jvc8463@gmail.com
• GitHub: @jvc-Byte

License

Copyright 2024 jvc-Byte

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, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Acknowledgments

• CDC BRFSS 2020 survey data
• Streamlit framework
• XGBoost library
• Healthcare professionals who validated the model

Disclaimer

This tool is for educational purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment. Always seek the guidance of qualified healthcare professionals for medical concerns.

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Made with ❤️ by jvc-Byte

Built With

• git
• github
• numpy
• pandas
• python
• streamlit
• xgboost