Table 3.

The performance of each model was evaluated in the validation cohort.

Models	AUC	Sensitivity	Specificity	Precision	F1 score
LR	0.910	0.945	0.775	0.444	0.605
SVM	0.912	0.855	0.862	0.54	0.662
GBM	0.914	0.927	0.803	0.472	0.626
RF	0.891	0.855	0.824	0.48	0.614
NN	0.907	0.873	0.882	0.585	0.701
XGBoost	0.903	0.836	0.879	0.568	0.676
KNN	0.893	0.8	0.865	0.53	0.638

Abbreviation: AUC, area under the curve; LR, logistic regression; SVM, support vector machine; GBM, gradient boosting machine; RF, random forest; NN, neural network; XGBboost, extreme gradient boosting; KNN, k-nearest neighbor