Figure 3.

Comprehensive analysis of mutiple ML algorithms. (A) ROC and AUC value of ML models in training set. (B) ROC and AUC value of ML models in validation set. (C) PRs of ML models in training set. (D) PRs of ML models in validation set. (E) DCA of ML models in validation set. (F)The calibration curve of ML models in validation set. ML machine learning, ROC receiver operating characteristic curves, AUC area under the curve, PRs precision-recall curves, DCA decision curve analysis, DT decision tree, RF random forest, XGBoost Xtreme gradient boosting, LASSO least absolute shrinkage and selection operator, SVM support vector machine, MLP multilayer perceptron, LightGBM light gradient boosting machine, KNN K-nearest neighbor, bs brier score.