Table 4. AUCs of the 8 ML models in the diagnosis of APE in the training, testing, and validation groups.

ML models	AUCtraining	AUCtesting	AUCvalidation
Gradient boosting decision trees	0.772	0.857	0.810
Naïve Bayes	0.715	0.703	0.727
Decision tree	0.607	0.668	0.642
k-nearest neighbors	0.660	0.641	0.701
Logistic regression	0.746	0.732	0.748
Multilayer perceptron	0.686	0.701	0.677
Random forest	0.763	0.715	0.731
Support vector machine	0.738	0.744	0.735

AUC, area under curve; ML, machine learning; APE, acute pulmonary thromboembolism.