Table 3. Classifier performance measures based on the confusion matrix method.

Accuracy	$\frac{TP+TN}{TP+TN+FP+FN}$
Sensitivity (True Positive Rate)	$\frac{TP}{TP+FN}$
Specificity (True Negative Rate)	$\frac{TN}{TN+FP}$

Accuracy was computed as the proportion of correctly classified samples. Sensitivity and specificity were computed as the rate of correctly predicted samples in the positive and negative labeled class, respectively (TP: true positive; TN: true negative; FP: false positive; FN: false negative).