Table 5.

Performance metrics of the meta-classifier on test set.

Symbol	Performance Metric	Definition as	What Does It Measure?	Value
CCR	Correctly Classified instance Rate—Accuracy	(TP + TN)/(TP + TN + FP + FN)	How good the model is at correctly predicting both positive and negative cases	0.9904
TPR	True Positive Rate—Sensitivity—Recall	TP/(TP + FN)	How good the model is at correctly predicting positive cases	0.9908
FPR	False Positive Rate—Fall-out	FP/(FP + TN)	Proportion of incorrectly classified negative cases	0.010
PPV	Positive Predictive Value—Precision	TP/(TP + FP)	Proportion of correctly classified positive cases out of total positive predictions	0.9908
AUC	ROC Area	Area under the ROC curve	Area under plot of TPR against FPR	0.997