What question did this study set out to answer?

The aim is to create and validate an interpretable machine learning model to predict CMM risk in T2DM patients for early intervention.

January 23, 2026Open Access

An online interpretable machine learning model for predicting cardiometabolic multimorbidity risk in patients with type 2 diabetes mellitus

Puntos clave

The aim is to create and validate an interpretable machine learning model to predict CMM risk in T2DM patients for early intervention.
Used data from 793 T2DM patients, divided into training and validation sets.
Employed recursive feature elimination and random forest for feature selection.
Applied six machine learning algorithms to develop the risk model.
Evaluated model performance using metrics like accuracy, precision, recall, F1-score, and AUC.
Model interpretability was achieved using SHAP and LIME.
The Stacking model achieved the highest AUC of 0.868 in internal validation.
External validation performance was strong with an AUC of 0.822.
Nine predictors were identified as significant in the risk model.

Resumen

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.

Leer artículo completoexternamente

Me gusta

Guardar

Ver artículo completo