Table 2.

Comparison of the 5 machine learning–based models by metric.

Model	Values, mean (SD)
	ACCa	Senb	Spec	PPVd	NPVe	AUROCf
LRg	0.75 (0)	0.624 (0.005)	0.828h (0.004)	0.686 (0.005)	0.786 (0.005)	0.824 (0.002)
SVMi	0.75 (0)	0.624 (0.005)	0.828 (0.004)	0.686 (0.005)	0.784 (0.005)	0.824 (0.002)
RFj	0.77 (0)	0.696 (0.005)	0.818 (0.004)	0.696 (0.005)	0.818 (0.004)	0.85 (0.002)
MLPk	0.758 (0.004)	0.642 (0.017)	0.822 (0.007)	0.686 (0.005)	0.792 (0.007)	0.831 (0.005)
XGBl	0.782 (0.004)	0.716 (0.005)	0.824 (0.005)	0.71 (0)	0.828 (0.004)	0.865 (0.002)

^aACC: accuracy.

^bSen: sensitivity.

^cSpe: specificity.

^dPPV: positive predictive value.

^eNPV: negative predictive value.

^fAUROC: area under the receiver operating characteristic.

^gLR: logistic regression.

^hThe italicized values refer to the highest score of each metric.

ⁱSVM: support vector machine.

^jRF: random forest.

^kMLP: multilayer perceptron.

^lXGB: extreme gradient boosting_.