kbsss/Heart-Attack-Risk-Rate

❤️ 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. 1.Enter your health information in the input form
2. 2.Click "Predict Heart Attack Risk"
3. 3.View your personalized risk assessment with:
4. 4.Overall risk percentage
5. 5.Individual model predictions
6. 6.Key risk factors identified
7. 7.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.

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