Table 2.

The predictive performance of models in the training and external validation cohorts.

Models	Training cohort
	AUC	Cutoff	Sensitivity	Specificity	PPV	NPV
LRM	0.812 (0.793–0.832)	0.335	0.673 (0.639–0.706)	0.809 (0.790–0.826)	0.597 (0.564–0.629)	0.855 (0.837–0.871)
RF	0.855 (0.838–0.873)	0.316	0.697 (0.663–0.728)	0.845 (0.828–0.861)	0.653 (0.621–0.686)	0.868 (0.852–0.884)
XGBoost	0.899 (0.884–0.914)	0.315	0.799 (0.769–0.826)	0.830 (0.812–0.847)	0.664 (0.633–0.694)	0.907 (0.893–0.921)
MLP	0.824 (0.805–0.843)	0.356	0.675 (0.641–0.707)	0.821 (0.803–0.838)	0.613 (0.580–0.646)	0.857 (0.840–0.873)
SVM	0.823 (0.804–0.842)	0.295	0.722 (0.689–0.753)	0.787 (0.768–0.806)	0.588 (0.557–0.619)	0.870 (0.853–0.886)
Simplified SVM	0.810 (0.790–0.830)	0.271	0.713 (0.680–0.744)	0.768 (0.748–0.787)	0.564 (0.532–0.595)	0.864 (0.846–0.880)

Models	External validation cohort
	AUC	Cutoff	Sensitivity	Specificity	PPV	NPV
LRM	0.739 (0.691–0.786)	0.293	0.634 (0.557–0.706)	0.733 (0.684–0.778)	0.529 (0.459–0.599)	0.809 (0.762–0.850)
RF	0.689 (0.639–0.739)	0.273	0.663 (0.587–0.733)	0.651(0.599–0.699)	0.473 (0.409–0.538)	0.803 (0.753–0.847)
XGBoost	0.716 (0.668–0.765)	0.246	0.622 (0.545–0.695)	0.738 (0.690–0.783)	0.530 (0.458–0.600)	0.805 (0.758–0.846)
MLP	0.731 (0.683–0.779)	0.323	0.610 (0.533–0.684)	0.755(0.707–0.798)	0.541 (0.468–0.613)	0.804 (0.757–0.844)
SVM	0.759 (0.713–0.805)	0.337	0.634 (0.557–0.706)	0.796 (0.751–0.836)	0.595 (0.521–0.667)	0.821 (0.777–0.860)
Simplified SVM	0.776 (0.731–0.821)	0.238	0.738 (0.666–0.802)	0.686 (0.635–0.733)	0.527 (0.462–0.591)	0.846 (0.801–0.886)

AUC: the area under the receiver operating characteristic curve; LRM: logistic regression model; MLP: multilayer perceptron; NPV: negative predictive value; PPV: positive predictive value; RF: random forest; SVM: support vector machine; XGBoost: extreme gradient boosting.