Table 2. Performance metrics of six machine learning algorithms in the training and test set

Performance metrics of six machine learning algorithms in the training set
Model	AUC	CA	F1	Precision	Recall
Gradient boosting	0.812	0.792	0.786	0.795	0.792
Random forest	0.809	0.775	0.765	0.783	0.775
Neural network	0.675	0.650	0.639	0.642	0.650
Logistic regression	0.665	0.633	0.622	0.624	0.633
Decision tree	0.653	0.642	0.643	0.645	0.642
Support vector machine	0.577	0.650	0.623	0.645	0.650
Performance metrics of six machine learning algorithms in the test set
Model	AUC	CA	F1	Precision	Recall
Gradient boosting	0.867	0.766	0.722	0.619	0.867
Random forest	0.822	0.733	0.778	0.667	0.933
Logistic regression	0.613	0.567	0.629	0.550	0.733
Decision tree	0.538	0.533	0.562	0.529	0.600
Support vector machine	0.493	0.467	0.619	0.481	0.867
Neural network	0.467	0.433	0.564	0.458	0.733