Table 2.

Overview of performance metrics.

Metric	Formula	Description
Sensitivity (SV)	$\frac{\mathrm{T}\mathrm{P}}{\mathrm{T}\mathrm{P}+\mathrm{F}\mathrm{N}}$	It measures the portion of positives that are correctly identified (performance measure of the whole positive of a dataset)
Specificity (SP)	$\frac{\mathrm{T}\mathrm{N}}{\mathrm{T}\mathrm{N}+\mathrm{\hspace{0.17em}\hspace{0.17em}}\mathrm{F}\mathrm{P}}$	It measures the portion negatives that are correctly identified (performance measure of the whole negative part of a dataset)
Positive Predictive Value (PPV)	$\frac{\mathrm{T}\mathrm{P}}{\mathrm{T}\mathrm{P}+\mathrm{F}\mathrm{P}}$	The ratio of correctly diagnosed positives to the total of identified positives
Negative Predictive Value (NPV)	$\frac{\mathrm{T}\mathrm{N}}{\mathrm{T}\mathrm{N}+\mathrm{F}\mathrm{N}}$	The ratio of correctly diagnosed negatives to the total of identified negatives
Accuracy (ACC)	$\frac{\mathrm{T}\mathrm{P}+\mathrm{T}\mathrm{N}}{\mathrm{T}\mathrm{P}\mathrm{\hspace{0.17em}\hspace{0.17em}}+\mathrm{F}\mathrm{P}+\mathrm{T}\mathrm{N}\mathrm{\hspace{0.17em}\hspace{0.17em}}+\mathrm{F}\mathrm{N}}$	The ratio of correctly diagnosed cases to the total diagnosed cases ( the overall performance measure)
Area under the receiver operating characteristics curve (AUC-ROC)	Graphical plot [13]	In a Receiver Operating Characteristics (ROC) curve the sensitivity is plotted in function of the false positive rate (100-Specificity) for different cut-off points of a parameter. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. The area under the ROC curve (AUC-ROC) is a measure of how well a parameter can distinguish between two diagnostic groups (diseased/normal)

TP: true positive (number of positive cases correctly detected).

TN: true negative (number of negative cases correctly detected).

FP: false positive (number of negative cases incorrectly detected as positive).

FN: false negative (number of positive cases incorrectly detected as negative).