Fig. 7

XGBoost model identifies patients with severe CHD. (A) 10-fold cross-validation ROC curves in the training set. (B) 10-fold cross-validation ROC curves in the validation set. (C) ROC curve in the test set. (D) The learning curves exhibit similar trends between the training and validation sets with small differences, indicating strong generalization capabilities and near-optimal performance. (E) Decision curve analysis reveals that the model outperforms alternative treatment strategies and is clinically effective. (F) The calibration curve reveals the model’s overconfidence in predicting severe CHD