Translational Evaluation of Interpretable Machine Learning for Cardiovascular Risk Prediction: Calibration Decomposition, Subgroup Audits, and Decision-Utility Analysis
Cardiovascular disease risk prediction models are often evaluated primarily by discrimination, although translational decision making depends on well-calibrated probabilities, subgroup reliability, and demonstrated clinical utility at actionable risk thresholds. This study conducted a translational evaluation of interpretable machine-learning models for heart disease prediction using a deployment-oriented framework integrating discrimination, calibration (including Murphy decomposition), explainability, subgroup stability, and decision-utility analysis via decision curve analysis. Using a large secondary dataset (308,774 observations; 19 predictors; prevalence 8.1%), models were trained with a stratified hold-out design and evaluated on a fixed test set. Histogram-based gradient boosting achieved the strongest discrimination (PR-AUC 0.3177; AUROC 0.8407) and strong probabilistic accuracy (Brier score 0.0633; ECE 0.0045), with Murphy decomposition indicating minimal reliability loss while preserving resolution. Explainability analyses (SHAP with PDP/ALE/ICE diagnostics) enabled transparent assessment of feature contributions and nonlinear effects relevant to plausibility and governance. Subgroup analyses indicated broadly stable discrimination but more variable calibration across age and self-reported general health strata, supporting the need for subgroup-aware monitoring. Decision curve analysis demonstrated positive net benefit relative to treat-all and treat-none strategies across screening-relevant thresholds (0.05–0.15), with workload trade-offs informing threshold selection for practice.