Heart Disease Predictor
Project Information
View on GitHub- Category: Healthcare ML
- Technologies: Scikit-learn Pandas Numpy XGBoost
- Project Date: January 2024
- Dataset: UCI Heart Disease Dataset
- Accuracy: 87.5% (5-fold cross-validation)
Heart Disease Predictor
A machine learning model that predicts the likelihood of heart disease based on medical attributes and lifestyle factors.
Technical Implementation
- Evaluated 8 different classification algorithms
- Comprehensive feature engineering pipeline
- Risk probability calibration system
- SHAP values for explainable AI
Key Features
- Interactive feature importance visualization
- Individualized risk assessment reports
- Exportable prediction results
- Responsive web interface for medical professionals
