Explainable extratreeclassifier model for early detection of type 2 diabetes: evidence from the PERSIAN Dena Cohort

Abstract

BACKGROUND: Type 2 diabetes mellitus (T2DM) develops gradually and often remains undiagnosed until complications emerge. Early detection through transparent machine-learning models can improve prevention and targeted screening. This study developed and evaluated an interpretable Extra Trees Classifier (ETC) for early detection of T2DM within the PERSIAN Dena Cohort, emphasizing probability calibration, fairness, and clinical interpretability. METHODS: Data from 3,203 adults aged 35–70 years were analyzed. Seventy-nine demographic, lifestyle, anthropometric, comorbidity, and biochemical variables were considered; fifteen informative predictors were retained after preprocessing and feature elimination. The ETC was optimized by randomized hyperparameter search and evaluated through ten-fold cross-validation with an additional 80 / 20 internal–external split. Isotonic regression was used to calibrate probability estimates. Model transparency and feature influence were examined using SHapley Additive exPlanations (SHAP) and Morris sensitivity analysis. RESULTS: Cross-validated performance showed mean accuracy 0.69 ± 0.03 and AUC 0.69 ± 0.04, indicating moderate discrimination and stable internal consistency. On the 20% hold-out set, the uncalibrated model achieved AUC 0.67 and F1 0.66. After isotonic calibration, AUC declined to 0.64 and the Brier score increased to 0.48 (slope 0.09; intercept − 1.50), revealing under-confident probability estimates. Excluding fasting blood sugar (FBS) improved performance (AUC 0.77), whereas categorizing FBS into deciles reduced AUC to 0.57. Across sex and age subgroups, AUCs ranged 0.63–0.70 without systematic bias. SHAP and Morris analyses identified FBS, fatty-liver status, age, kidney-stone history, and triglycerides as dominant predictors, with lifestyle factors such as beverage and vegetable intake exerting secondary, modifiable influence. CONCLUSIONS: Although overall predictive power was limited, the calibrated ETC provided transparent insight into feature interactions, calibration behavior, and data limitations. The framework highlights that interpretability and fairness are as essential as accuracy for trustworthy clinical AI. Future research should expand predictor diversity, address class imbalance, and validate across other PERSIAN cohorts to develop a more generalizable, interpretable model for early T2DM risk prediction.

Topics & Concepts