A hybrid ensemble stacking and explainable machine learning framework enhanced with bald eagle search optimization for coronary artery disease risk prediction

Coronary artery disease (CAD) is a leading cause of mortality worldwide, necessitating early, accurate, and non-invasive prediction methods. While machine learning offers a promising alternative to conventional invasive diagnostics, challenges such as high-dimensional data, class imbalance, and redundant features remain. This study proposes HESML-CAD, a hybrid ensemble stacking and meta-learning framework enhanced with Bald Eagle Search Optimization (BESO) and explainable AI for reliable and interpretable CAD prediction. The model was evaluated on two datasets: Framingham (4200 samples, 15 features) for 10-year risk prediction and Z-Alizadeh Sani (304 samples, 55 features) for current diagnosis. Preprocessing included imputation, normalization, encoding, and ADASYN-based balancing. Feature selection combined Chi-square, mutual information, autoencoder-based reduction, and BESO optimization. Five base classifiers (KNN, SVM-RBF, Random Forest, XGBoost, and CatBoost) were stacked using Logistic Regression, with Platt scaling for calibration and SHAP for explainability.On the Framingham dataset, HESML-CAD achieved an accuracy of 0.93, F1-score of 0.93, and AUC of 0.96, outperforming the best individual model (Random Forest: accuracy 0.896, F1-score 0.90) by approximately 3–4%, with a low Brier score of 0.0540. On the Z-Alizadeh Sani dataset, it achieved an accuracy of 0.94, F1-score of 0.935, and AUC of 0.98, surpassing strong baselines (accuracy ≈ 0.93), while maintaining well-calibrated probability estimates (Brier score 0.0547). SHAP identified clinically relevant predictors such as age, systolic blood pressure, and chest pain. Overall, HESML-CAD provides accurate, calibrated, and interpretable CAD predictions, demonstrating potential for supporting non-invasive clinical screening.