Table 2.

Performance evaluation metric of the proposed model.

Metric	Formula	Description
Precision	$\frac{TP}{TP+FP}$	Indicates the proportion of positive identifications which were correct.
Recall	$\frac{TP}{TP+FN}$	Indicates the proportion of actual positives which were correctly classified
F1-score	$2\times \frac{\left(Precision\times Recall\right)}{\left(Precision+Recall\right)}$	Combination of precision and recall
Accuracy	$\frac{TP+FP}{\left(TN+TP+FN+FP\right)}$	Overall performance of the model
AUC-ROC	$\frac{1+TPR-FPR}{2}$	Comparison of a model’s TPR versus model’s FPR