Interpretable machine learning for predicting mild cognitive impairment in elderly patients with type 2 diabetes mellitus: model development and performance assessment.

Type 2 diabetes mellitus (T2DM) is a prevalent chronic condition, particularly in the elderly, and is associated with an increased risk of cognitive decline, including mild cognitive impairment (MCI). This study aimed to develop and validate an interpretable machine learning (IML) model to predict MCI in elderly T2DM patients using routine clinical data. A retrospective cohort of 923 elderly T2DM patients (≥ 60 years) was analyzed, with data collected from January 2021 to January 2025. Key MCI predictors were selected using a two-stage feature selection process involving Boruta and least absolute shrinkage and selection operator (LASSO). Six machine learning (ML) algorithms-logistic regression (LR), extreme gradient boosting (XGBoost), support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and decision tree (DT)-were trained and evaluated. SHapley Additive exPlanations (SHAP) were employed to interpret model predictions and provide insights into feature importance. Among 923 participants, 424 (45.9%) had MCI. Baseline characteristics were comparable between the training and validation sets, with similar MCI prevalence (46.1% vs. 45.5%). Seven predictors were consistently selected: age, years of education, duration of diabetes, regular physical activity, cerebrovascular disease, glycated hemoglobin (HbA1c), and fasting plasma glucose (FPG). Among the six models, the RF model demonstrated the best overall performance, achieving an AUC of 0.842 in the validation set, with favorable discrimination, calibration, and net clinical benefit. SHAP analysis identified duration of diabetes as the most influential predictor, followed by HbA1c and FPG, emphasizing the role of both cumulative and current glycemic burden. An interpretable RF-based model using routine clinical data effectively predicted MCI in elderly patients with T2DM and provided clinically intuitive explanations of risk drivers. This approach may support risk-stratified cognitive screening and individualized management; external multicenter validation and prospective evaluation are warranted.