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arXiv · 2606.16056

Beyond the Blood Draw: Explainable Machine Learning for Non-Invasive Dysglycemia Risk Screening

Black Sun·Chenyi Zhang·Kaiyi Ji·Xi Lu
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Abstract

Dysglycemia, encompassing both prediabetes and diabetes, affects huge numbers of adults worldwide, yet many of them remain undiagnosed. We developed and validated machine-learning (ML) models for non-invasive screening of dysglycemia risk that require no laboratory tests. Pooling data from the National Health and Nutrition Examination Survey (NHANES) 2017--2023 (n=14,352), we trained six ML models with stratified 5-fold cross-validation and compared them with two established clinical risk scores. LightGBM achieved the highest area under the receiver operating characteristic curve (AUC=0.820, 95% CI: 0.806--0.835), outperforming the Finnish Diabetes Risk Score (0.745) and American Diabetes Association Risk Test (0.783). SHAP analysis identified age, race/ethnicity, and waist-to-height ratio as the most influential predictors. Subgroup analyses confirmed consistent performance across demographic strata (AUC: 0.735--0.832). These results demonstrate the feasibility of explainable, laboratory-free dysglycemia screening for deployment in community settings and self-tracking health applications.

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Black Sun, Chenyi Zhang, Kaiyi Ji, Xi Lu. 2026-06-14. Beyond the Blood Draw: Explainable Machine Learning for Non-Invasive Dysglycemia Risk Screening. https://arxiv.org/abs/2606.16056

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