Table 4.

The machine learning techniques for the validation set.

	AUC (95% CI)	Cutoff (95% CI)	Accuracy (95% CI)	Sensitivity (95% CI)	Specificity (95% CI)	F1 score (95% CI)
XG Boost	0.801 (0.701–0.901)	0.48 (0.323–0.637)	0.662 (0.611–0.713)	0.969 (0.908–1.030)	0.652 (0.494–0.811)	0.734 (0.602–0.866)
Logistic	0.841 (0.754–0.927)	0.245 (0.231–0.259)	0.708 (0.644–0.771)	0.977 (0.933–1.022)	0.636 (0.601–0.671)	0.671 (0.587–0.756)
Random Forest	0.781 (0.675–0.887)	0.5 (0.402–0.598)	0.721 (0.708–0.734)	0.855 (0.711–0.999)	0.672 (0.595–0.749)	0.735 (0.718–0.753)

AUC = area under the curve, 95% CI = 95% confidence interval.