Identification of key factors and explainability analysis for surgical decision-making in hepatic alveolar echinococcosis assisted by machine learning

Figure 2 Model performance and validation. A: Receiver operating characteristic (ROC) curves of 11 machine learning models; B: ROC curves of the eXtreme Gradient Boosting (XGBoost) model on the training and validation sets after parameter optimization; C: Calibration curve of the XGBoost model, showing consistency between predicted probabilities and observed proportions; D: Decision Curve Analysis of the XGBoost model, demonstrating net clinical benefit. ROC: Receiver operating characteristic; XGBoost: EXtreme Gradient Boosting; DCA: Decision curve analysis; kNN: K-Nearest Neighbors; SVM: Support Vector Machine; GP: Gaussian Process; LR: Logistic Regression; MN: Neural Network; RF: Random Forest; GBM: Gradient Boosting Machine; C5.0: C5.0 Decision Tree; Ada: AdaBoost.