Beyond Glycemic Control: Precision Medicine in Type 2 Diabetes Using Multi-Output Explainable Artificial Intelligence for Personalized SGLT2 and DPP-4 Therapy Selection

Traditional treatment strategies for Type 2 diabetes (T2D) adopt a “one-size-fits-all” approach, limiting individual effectiveness. This study presents an explainable, data-driven framework for multi-treatment and single-treatment selection of SGLT2 inhibitors (SGLT2-i) and DPP-4 inhibitors (DPP4-i) based on patient-specific health characteristics. Our approach evaluates treatment effectiveness across four outcomes—glycosylated hemoglobin (HbA1c), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and body mass index (BMI)—to enable individualized treatment recommendations. The multi-treatment model, based on multi-output regression, achieved an R2 score of 0.44 and an RMSE of 5.58, identifying benefit subgroups for SGLT2-i and DPP4-i across all outcomes. Integrated with SHapley Additive exPlanations (SHAP) analysis, the model offers insights into the factors influencing treatment effects. The single-treatment selection algorithm achieved an accuracy of 0.47 and an F1 score of 0.46, showing a higher average treatment effect with SGLT2-i on all outcomes, notably in the reduction in HbA1c, LDL, and BMI and a modest increase in HDL. While DPP4-i demonstrated beneficial effects on HbA1c, LDL, and HDL, it was associated with an increase in BMI. These findings highlight the benefits of a multi-faceted, patient-centered precision medicine approach for T2D management, enabling treatment strategies that address individual health needs beyond HbA1c.