README.md

---
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**