Table 3.

The performance for each of the models.

	Decision tree	Random forest	XGboost	Support vector machine	Neural network	K-nearest neighbors	Nomogram
Accuracy	0.68	0.74	0.63	0.73	0.66	0.70	0.66
Precision	0.70	0.74	0.79	0.77	0.70	0.70	0.70
Recall	0.84	0.89	0.55	0.81	0.79	0.90	0.79
F1 score	0.77	0.81	0.65	0.79	0.74	0.79	0.74
Sensitivity	0.84	0.89	0.55	0.81	0.79	0.90	0.79
Specificity	0.41	0.49	0.77	0.60	0.44	0.36	0.44
AUC score	0.63	0.80*	0.72	0.80	0.69	0.76	0.69

*Statistical significance of differences in AUC scores between Random forest and Nomogram (tested by the DeLong test). AUC, area under the curve.