Integrating conformal prediction with machine learning for uncertainty-aware risk stratification in coronary artery disease.

Background: Predictive models are increasingly used in clinical decision-making in coronary artery disease (CAD). However, most existing models focus on discriminative ability while ignoring individual prediction uncertainty, which is particularly prominent in small-sample contexts, limiting clinical applications. Methods: We developed an uncertainty-aware model integrating machine learning and conformal prediction (CP) for CAD prediction in a small-sample setting cohort ( Results: Both LR and RF show strong discrimination (AUC 0.953 and 0.951). LR is slightly better calibrated, while RF is more accurate. CP efficiently quantifies uncertainty. Union and intersection achieve higher coverage, whereas MSCP with voting balances coverage and efficiency. The combined RF-MSCP model maintains empirical coverage above the nominal level across all significance thresholds, demonstrating robust uncertainty assessment and risk prediction in small-sample settings. Conclusion: The integration of CP with machine learning has led to the development of the RF-MSCP model, which provides reliable uncertainty quantification for CAD risk prediction, especially in small sample settings. By identifying potentially high-risk individuals overlooked by traditional models, this approach improves predictive discrimination and supports more robust clinical decision.