Non-invasive opportunistic screening for diabetes mellitus: an interpretable stacking ensemble framework

Abstract

Abstract Opportunistic screening for type 2 diabetes offers a potentially accessible approach to preliminary case detection without relying on invasive testing. In this study, we developed a heterogeneous Stacking ensemble model (Task C) using exclusively non-invasive demographic, lifestyle, medical-history, and symptom-based features. The model prioritized sensitivity, achieving a Recall of 0.9267, while showing modest discriminative performance (AUC = 0.5515), low specificity (0.1106), and moderate probability calibration (Brier Score = 0.2482). Targeted simulation analyses revealed that adjusting the top three modifiable behavioral factors captured approximately 85.5% of the reduction in model-estimated screening probability observed under the all-six-factor adjustment. Individual-level case simulation illustrated a stepwise reduction in model-estimated screening probability under increasingly comprehensive hypothetical adjustments. Decision curve analysis suggested potential screening utility mainly within the lower-threshold range. These findings suggest that the proposed ensemble may serve as a technically feasible and interpretable tool for preliminary non-invasive diabetes case-finding, while providing hypothesis-generating insights into modifiable factors for future validation.