Adaptive Risk-Stratified Stacking for Ten-Year Cardiovascular Disease Prediction with SHAP Interpretability - Sorn-In, Kanda; Chansanam, Wirapong; Netayawijit, Pathamakorn (Engineering, Technology & Applied Science Research, 2026)
Cardiovascular Disease (CVD) remains the leading cause of death worldwide, accounting for over 17.9 million deaths annually. Traditional risk assessment tools such as the Framingham Risk Score and Atherosclerotic Cardiovascular Disease (ASCVD) calculator are constrained by linear assumptions and limited variables, often failing to capture complex interactions among clinical and behavioral factors. To overcome these limitations, this study proposes an Adaptive Risk-Stratified Stacking (ARSS) framework that integrates ensemble learning, Explainable Artificial Intelligence (XAI), and Bayesian uncertainty quantification for ten-year CVD prediction. Using data from the Framingham Heart Study (FHS) (n = 4,240; 16 features), the framework combines Random Forest, Extreme Gradient Boosting (XGBoost), and Logistic Regression as base learners, with a Logistic Regression meta-classifier trained using five-fold stratified cross-validation. The adaptive stratification mechanism enables subgroup-specific learning across low-, intermediate-, and high-risk cohorts, enhancing personalization and sensitivity. The ARSS model achieved 89.6% accuracy, an F1-score of 0.89, and an area under the receiver operating characteristic curve (ROC–AUC) of 0.918 (95% Confidence Interval (CI): 0.907–0.929), significantly outperforming baseline models (p < 0.01, Cohen's d ≥ 0.71). Calibration analysis indicated strong reliability (Brier Score = 0.076), whereas Shapley Additive Explanations (SHAP)-based interpretability revealed clinically consistent feature interactions such as Age × Systolic Blood Pressure and Diabetes × Glucose, reinforcing the model's physiological plausibility. Bayesian uncertainty estimation further enhanced confidence in predictive reliability and transparency. Overall, the proposed ARSS framework demonstrates that interpretable, risk-stratified ensemble learning can bridge predictive accuracy with clinical trustworthiness, establishing a unified and ethical paradigm for XAI in precision cardiovascular prevention.