Sex-Specific Explainable Machine Learning Models of Diabetic-Range 120-Minute OGTT Glucose Using Non-Invasive Variables
Year: 2026
Type: article
Abstract: Background/Objectives: This study evaluated whether sex-specific machine learning models using non-invasive variables could identify individuals with glucose concentrations of ≥200 mg/dL at 120 min during an oral glucose tolerance test (OGTT) and examined the characteristics contributing to model predictions. Methods: Baseline data from 8555 participants (4055 men and 4500 women) in the Korean Genome and Epidemiology Study (KoGES) Ansan and Ansung cohort were analyzed separately by sex. Five classification models were developed using 20 non-invasive variables, with class weighting where supported and F2-based hyperparameter and threshold optimization. Performance was evaluated on held-out test sets, and SHapley Additive exPlanations (SHAP) were used for model interpretation. Results: The area under the receiver operating characteristic curve (AUROC) ranged from 0.579 to 0.665 in men and from 0.656 to 0.735 in women. The highest F2 scores were 0.276 (logistic regression) in men and 0.300 (random forest) in women. Body fat mass in men and age in women ranked among the top three variables in four of the five models, and blood pressure was frequently important in both sexes. Conclusions: The models showed modest predictive performance, with low precision limiting their use for OGTT selection. Model explanations identified non-invasive characteristics contributing to positive predictions that may inform clinical risk-factor review alongside glucose testing. Whether these explanations improve clinical decisions or patient outcomes remains to be established.
Source: Medical Sciences
Authors: YongJun Kim, JooYong Park
Institution: Eulji University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,689
OpenAlex ID: W7225629383