Table 3.

The diagnostic efficacy of 5 machine learning models in the training set and validation set.

Index		AUC*	Accuracy	Sensitivity	Specificity	Precision	Recall	F1
									Training set	DT	1 (0.99, 1.00)	0.96	0.76	0.66	0.85	0.86	0.81
Logistic	0.92 (0.90, 0.95)	0.86	0.84	0.89	0.78	0.75	0.77
XGBoost	1 (0.99, 1.00)	0.97	0.82	0.79	0.91	0.89	0.87
KNN	0.93 (0.92, 0.95)	0.86	0.88	0.75	0.75	0.46	0.62
NB	0.91 (0.90, 0.93)	0.83	0.70	0.56	0.69	0.56	0.62
Validation set	DT	0.79 (0.78, 0.81)	0.80	0.83	0.62	0.53	0.45	0.48
	Logistic	0.93 (0.91, 0.94)	0.87	0.88	0.86	0.76	0.75	0.67
	XGBoost	0.91 (0.90, 0.92)	0.80	0.87	0.79	0.58	0.58	0.58
	KNN	0.92 (0.90, 0.93)	0.83	0.86	0.71	0.75	0.38	0.52
	NB	0.89 (0.87, 0.90)	0.82	0.61	0.51	0.61	0.45	0.51

*Data in parentheses are 95% confidence intervals.