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Interpretable type 2 diabetes incidence prediction with AutoScore: A model based on standard clinical parameters
Journal article   Peer reviewed

Interpretable type 2 diabetes incidence prediction with AutoScore: A model based on standard clinical parameters

Andreas Leiherer, Laura Schnetzer, Sylvia Mink, Arthur Mader, Axel Mündlein, Bernhard Bermeitinger, Angela P. Moissl-Blanke, Winfried März, Angelika Hammerer-Lercher, Marcus E. Kleber, …
International journal of medical informatics (Shannon, Ireland), v 206, 106161
01 Feb 2026
DOI: https://doi.org/10.1016/j.ijmedinf.2025.106161
PMID: 41176846
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Artificial intelligence Biomarker Cardiovascular risk Diabetes incidence Risk prediction Machine Learning
[Display omitted] •We compared an interpretable AutoScore model with an optimized SVM to predict incident T2DM in cardiovascular risk patients.•AutoScore used routine clinical parameters and achieved strong performance (AUC 0.69), slightly below that of SVM (AUC 0.72).•Both models consistently identified fasting glucose, OGTT glucose, and the Matsuda index as key predictors.•AutoScore’s transparency and simplicity offer advantages for clinical use, especially in routine, resource-limited settings. Accurate prediction of type 2 diabetes mellitus (T2DM) onset is critical to enable timely interventions and preventive strategies. Although machine learning (ML) approaches have shown promise in risk prediction, their complexity often limits clinical implementation. There is a need for interpretable, user-friendly models that retain predictive strength. We studied 904 cardiovascular risk patients without T2DM at baseline, assessing 71 anthropometric, clinical, and laboratory variables. Over a four-year follow-up, 10 % developed T2DM. We applied AutoScore, an interpretable ML framework that generates parsimonious, point-based risk scores, and compared its performance with an optimized Support Vector Machine (SVM) with a linear kernel. The SVM was refined using feature selection, Tomek link removal, and up-sampling to address class imbalance. Both approaches consistently identified fasting glucose, OGTT glucose, and the Matsuda index (reflecting glucose-insulin dynamics) as key predictors. The optimized SVM model achieved a higher balanced accuracy (75 % vs. 67 %), specificity (80 % vs. 77 %), and AUC (0.72 vs. 0.69) compared to AutoScore. However, AutoScore, other than the SVM model, relied exclusively on a small set of routinely available accessible parameters and thereby offered superior interpretability and ease of integration into clinical workflows. External validation in an independent cohort further confirmed the robustness of the AutoScore model. Although black-box models such as SVM deliver slightly higher predictive accuracy, interpretable frameworks like AutoScore provide clinically actionable risk stratification based on standard data. Their transparency and simplicity make them particularly valuable for real-world decision support.

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UN Sustainable Development Goals (SDGs)

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Industry collaboration
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International collaboration
Web of Science research areas
Computer Science, Information Systems
Health Care Sciences & Services
Medical Informatics
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