Cardiometabolic multimorbidity (CMM), a major complication in type 2 diabetes mellitus (T2DM), increases mortality and healthcare burden. Early identification of high-risk individuals is crucial for precision intervention. This study aimed to develop and validate an online interpretable machine learning system for forecasting the CMM risk in T2DM populations to facilitate personalized decision-making and early intervention. We used data from 793 T2DM patients from a tertiary hospital in Shanxi Province as the derivation cohort, divided into training (80%) and internal validation (20%) sets, with 360 cases from another independent center for external validation. Feature selection was performed through recursive feature elimination with random forest algorithm. We employed six machine learning algorithms to develop the CMM risk model. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC). The SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) provided model interpretability. After feature screening, nine predictors were included in the model. In internal validation, the Stacking model achieved the highest AUC (0.868), maintaining good external validation performance with an AUC of 0.822. The web-based system was accessible on https://t2dmcmmpredictionweb.streamlit.app/. This system assisted healthcare providers to identify high-risk populations early and facilitate timely intervention to mitigate disease progression.
Liu et al. (2026) studied this question.