README.md

metadata

title: Heart Attack Risk Prediction
emoji: ❤️
colorFrom: red
colorTo: pink
sdk: docker
sdk_version: 4.44.0
app_file: streamlit_app.py
pinned: false

❤️ Heart Attack Risk Prediction: An Ensemble Modeling Approach

Advanced machine learning ensemble combining XGBoost, CatBoost, and LightGBM for accurate cardiovascular risk assessment.

🎯 Features

• Ensemble Model: Combines XGBoost (5%), CatBoost (85%), and LightGBM (10%) for optimal performance
• High Accuracy: ~80.77% accuracy with ~93.27% recall
• Comprehensive Risk Assessment: Analyzes multiple health factors including:
  • Demographics (Age, Gender, Height, Weight)
  • Blood Pressure and Cholesterol levels
  • Lifestyle factors (Smoking, Alcohol, Physical Activity)
  • Derived health metrics (BMI, BP categories)

📊 Model Performance

• Accuracy: 80.77%
• Recall: 93.27%
• ROC-AUC: 92.50%

🚀 Usage

1. Enter your health information in the input form
2. Click "Predict Heart Attack Risk"
3. View your personalized risk assessment with:
   • Overall risk percentage
   • Individual model predictions
   • Key risk factors identified
   • Detailed model breakdown

🔬 Technical Details

Models Used

• XGBoost: Gradient boosting with optimized hyperparameters
• CatBoost: Categorical boosting with balanced class weights
• LightGBM: Light gradient boosting machine
• Ensemble: Weighted combination of all three models

Optimization

• Multi-objective optimization for accuracy and recall
• Threshold optimization for optimal performance
• Feature engineering with derived health metrics

📝 Citation

If you use this model in your research, please cite:

Heart Attack Risk Prediction: An Ensemble Modeling Approach
Using XGBoost, CatBoost, and LightGBM

⚠️ Disclaimer

This tool is for educational and research purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition.

📧 Contact

For questions or issues, please open an issue on the repository.

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Built with ❤️ using Streamlit, XGBoost, CatBoost, and LightGBM