Fig. 5

Performance evaluation of the XGBoost classification model. (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 curve shows that model performance in both the training set and the validation set improves steadily and that the difference between them remains stable, showing that the model neither overfits nor underfits the data and is able to effectively learn from the training data and generalize to the validation data. (E) The decision curve highlights the model’s excellent performance across multiple thresholds, indicating that it could help guide clinical decision-making. (F) The calibration curve shows close alignment between the predicted and actual probabilities, demonstrating the accuracy of the model’s predictive performance