Predictive Modelling of 3‐Month Glycemic Target Achievement in Type 2 Diabetes Mellitus Using Artificial Intelligence and Continuous Glucose Monitoring: A Retrospective Cohort Study

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AIMS: To develop and validate a machine learning model incorporating continuous glucose monitoring (CGM) metrics to predict 3-month glycemic target achievement in type 2 diabetes mellitus (T2DM) after short-term intensive insulin pump therapy followed by physician-selected maintenance treatment, and to build a web-based prediction tool. MATERIALS AND METHODS: We retrospectively included 1079 patients with T2DM and divided them into training, validation, and test sets (6:2:2). Eleven models were evaluated for discrimination, calibration, net benefit, and classification performance. Candidate models were compared for sensitivity to compression of correlated CGM predictors, interpretability, and suitability for web implementation. Nested logistic regression assessed the incremental value of CGM, and SHapley Additive exPlanations (SHAP) assessed feature contributions. RESULTS: SVM_RBF achieved the highest test-set area under the curve (AUC; 0.926), compared with 0.917 for XGBoost (paired DeLong p = 0.521). The models had similar test-set discrimination and probabilistic accuracy, with no consistent difference in calibration. XGBoost was selected for implementation because it was less sensitive to compression of correlated CGM predictors and supported TreeSHAP. Leading predictors included diabetes duration, age, fasting C-peptide (FCP), mean amplitude of glucose excursions (MAGE), body mass index (BMI), and time in range (TIR). Adding CGM improved continuous net reclassification improvement (0.308, p = 0.013) and integrated discrimination improvement (0.0062, p = 0.029), but not AUC or categorical net reclassification improvement. CONCLUSION: The XGBoost model estimates 3-month glycemic target achievement within this treatment pathway and was implemented as a web-based clinical decision-support tool.

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