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

Figure 2 Receiver operating characteristic curves comparing the predictive performance of multiple machine learning models for gestational diabetes mellitus prediction under different class imbalance handling strategies on the independent test set. A: Original dataset without resampling; B: Synthetic minority oversampling technique; C: Random undersampling; D: Random oversampling. The evaluated models included logistic regression, extreme gradient boosting, adaptive boosting, gradient boosting, light gradient boosting machine, support vector machine, random forest, and multilayer perceptron. The X-axis represents the false positive rate, and the Y-axis represents the true positive rate. The diagonal dashed line indicates random classification performance. The area under the curve values for each model are shown in the corresponding legends. SMOTE: Synthetic minority oversampling technique; RUS: Random undersampling; ROS: Random oversampling; LogReg: Logistic regression; XGBoost: Extreme gradient boosting; AdaBoost: Adaptive boosting; GradBoost: Gradient boosting; LightGBM: Light gradient boosting machine; SVM: Support vector machine; RandForest: Random forest; MLP: Multilayer perceptron; AUC: Area under the curve.