Model evaluation
REGRESSION
To determine the accuracy of the four predictive models, ROC curves with corresponding AUCs were calculated for the training cohort (Receving‐operating characteristic curves and AUCs demonstrating the predictions of the four models: LR, RF, SVM, and XGBoost. (A) The training cohort and (B) the validation cohort. AUC, area under the curve; LR, logistic regression; RF, random forest; SVM, support vector machine, XGBoost, extreme gradient boosting.In the validation cohort, the accuracy of the XGBoost model for predicting 5‐year survival status was superior (AUC = 0.8365, 95% CI: 0.8217–0.8513) than LR (AUC = 0.8217, 95% CI: 0.8063–0.8372), SVM (AUC = 0.8081, 95% CI: 0.7913–0.8249), and RF (AUC = 0.8354, 95% CI: 0.8205–0.8502), respectively. DeLong's tests have also shown that there were statistical differences between the XGBoost model and the other two predictive models (LR and SVM, both In our study, DCAs of the four predictive models for the training and validation cohorts were also performed (Figure Decision curves showing the predictions of the four models: LR, RF, SVM, and XGBoost. (A) The training cohort and (B) the validation cohort. LR, logistic regression; RF, random forest; SVM, support vector machine, XGBoost, extreme gradient boosting.