Development and validation of an interpretable machine learning model for predicting progression from prediabetes to type 2 diabetes

Figure 1  Study flowchart.

Figure 2 Performance of the gradient boosting survival analysis model in the external validation cohort. A: Kaplan-Meier curves comparing the cumulative incidence of type 2 diabetes mellitus in the high-risk and low-risk groups; B: Time-dependent receiver operating characteristic curves for predicting 1-year, 3-year, and 5-year progression to type 2 diabetes mellitus. AUC: Area under the curve.

Figure 3 Shapley Additive exPlanations analysis of feature importance for the gradient boosting survival analysis model in the external validation cohort. A: Beeswarm plot showing the distribution of Shapley Additive exPlanations values for each feature across individuals. Yellow indicates a positive contribution to the predicted risk, while purple indicates a negative contribution; B: Mean absolute Shapley Additive exPlanations values for each feature across 20 imputed datasets, with 95%CI. SHAP: Shapley Additive exPlanations; FBG: Fasting blood glucose; BMI: Body mass index; MONO: Monocyte count; HDL-C: High-density lipoprotein cholesterol; BASO: Basophil count; WBC: White blood cell count; TG: Triglycerides; ALT: Alanine aminotransferase; TC: Total cholesterol; TBil: Total bilirubin; FH: Family history; PDW: Platelet distribution width; MPV: Mean platelet volume.