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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月16日

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  • Lin Zhang,
  • Junming Xu,
  • Weigang Wang,
  • Rui Chang,
  • Linghua Wang

摘要

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.