Table 2.

Confusion matrix of prediction models in internal balanced and external imbalanced testing datasets

Dataset	Model	Accuracy	Sensitivity	Specificity	PPV	NPV	F1
Internal balanced	LR	0.88	0.91	0.85	0.86	0.91	0.89
	RF	0.88	0.90	0.85	0.86	0.89	0.88
	ANN	0.88	0.92	0.85	0.86	0.91	0.89
	SVM	0.88	0.91	0.86	0.86	0.91	0.89
	XGB	0.88	0.91	0.85	0.86	0.91	0.89
	DNN	0.87	0.89	0.86	0.86	0.88	0.87
External imbalanced	LR	0.97	0.60	0.98	0.46	0.99	0.52
	RF	0.97	0.51	0.98	0.42	0.99	0.46
	ANN	0.97	0.61	0.98	0.46	0.99	0.52
	SVM	0.95	0.64	0.96	0.31	0.99	0.42
	XGB	0.97	0.61	0.98	0.46	0.99	0.52
	DNN	0.97	0.58	0.98	0.43	0.99	0.50

PPV, positive predictive value; NPV, negative predictive value; LR, logistic regression; RF, random forest; ANN, artificial neural networks; SVM, support vector machines; XGB, extreme gradient boosting; DNN: deep neural network.