Early risk stratification of gestational diabetes using interpretable machine learning with first-trimester screening parameters

Figure 3 Model interpretability analysis of the gradient boosting model using SHapley Additive exPlanations. A: SHapley Additive exPlanations summary plot demonstrating the relative contribution and impact of clinical, biochemical, and ultrasound variables on gestational diabetes mellitus prediction; B: Heatmap of SHapley Additive exPlanations values across individual participants, illustrating feature importance patterns and model output distribution. Warmer colors indicate positive contributions to the predicted risk of gestational diabetes mellitus, whereas cooler colors indicate negative contributions. Greater color intensity reflects larger contribution magnitudes. SHAP: SHapley Additive exPlanations; MAP: Mean arterial pressure; BW: Weight; PAPP-A: Pregnancy-associated plasma protein A; PlGF: Placental growth factor; GDM: Gestational diabetes mellitus; BH: Body height; NT: Nuchal translucency; PI: Pulsatility index; CRL: Crown-rump length; β-hCG: β-human chorionic gonadotropin; PCOS: Polycystic ovary syndrome; IVF: In vitro fertilization; DM: Diabetes mellitus.