Table 3.

Performance metrics used.

No.	Performance Metric	Description
1	Accuracy	Accuracy is a measurement that gives the correctness of classification and loss is a measure indicating that how well a model behaves after every iteration.
2	Precision	The fraction of true positives (TP) from the total amount of relevant result. Precision = TP/(TP + FP).
3	Recall (Sensitivity)	The fraction of true positives from the total amount of TP and FN. Recall = TP/(TP + FN).
4	F1 Score	The harmonic mean of Precision and Recall given by the following formula: F1 = 2 ∗ (TP ∗ FP)/(TP + FP)
5	Specificity	Specificity = TN/(FP + TN)
6	Negative Predictive Value	NPV = TN/(TN + FN)
7	False Positive Rate	FPR = FP/(FP + TN)
8	False Discovery Rate	FDR = FP (FP + TP)
9	False Negative Rate	FNR = FN/(FN + TP)
10	Matthews Correlation Coefficient	TP ∗ TN − FP ∗ FN/sqrt((TP + FP) ∗ (TP + FN) ∗ (TN + FP) ∗ (TN + FN))