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

Performance metrics of five machine learning algorithms in the training set

Performing model

AUC

F1

Precision

Recall

Support vector machine

0.807

0.763

0.830

0.811

Random forest

0.800

0.800

0.840

0.832

Extreme gradient boosting

0.796

0.808

0.808

0.821

Logistic regression

0.696

0.748

0.758

0.786

Naïve Bayes

0.667

0.732

0.741

0.724

Performance metrics of five machine learning algorithms in the test set

Performing model

AUC

F1

Precision

Recall

Random forest

0.853

0.747

0.839

0.792

Extreme gradient boosting

0.824

0.761

0.800

0.792

Logistic regression

0.813

0.634

0.804

0.729

Support vector machine

0.790

0.675

0.815

0.75

Naïve Bayes

0.689

0.632

0.623

0.646