Web-based Explainable Machine Learning Model for Early-Stage Heart Disease Detection

• Proposed a web-based system using a Voting Ensemble (VE), a soft-voting classifier that combines K-Nearest Neighbors (KNN), Gradient Boosting (GB), and CatBoost with optimized hyperparameter for improved heart disease prediction.

• Evaluated model balance using 10-fold cross-validation with a held-out test set, ensuring robust performance and generalization.

• Integrated SHAP to give feature-level interpretability, enhancing clinical trust in model predictions.

• Developed a web-based application for real-time heart disease risk prediction, demonstrating potential for seamless clinical deployment.