Table 3.

Performance metrics of AI models.

SN	Performance Matrix	Description
1	Accuracy	It is set out as the number of correct predictions made as a ratio of all predictions made. $Accuracy=\frac{TP+TN}{TP+FP+FN+TN}$
2	Sensitivity or Recall	It is defined as the number of positive predictions made. $Sensitivity=\frac{TP}{TP+FN}$
3	Specificity	It is defined as the number of negative predictions made. $Specificity=\frac{TN}{TP+FN}$
4	Precision	It is defined as the number of correct positive results divided by the number of positive results predicted by the classifier. $Precision=\frac{TP}{TP+FP}$
6	F1-Score	It is defined as the weighted average of precision and recall. $F1-Score=\frac{2*\left(Precision*Recall\right)}{Precision+Recall}$
7	Area under ROC curve (AUC)	It is a probabilistic measure that defines how much the model is capable of distinguishing between classes.
8	Kaplan-Meier Curve	It is the visual representation of the function that shows the probability of an event at a respective time interval.
9	Mean Absolute Error (MAE)	It is defined as the average of the difference between the ground truth and the predicted values by the regression model. $\mathrm{MAE}=\frac{{{\displaystyle \sum }}_{i=0}^N\left|y_i-y_i^p\right|}{N}$
10	Mean Square Error (MSE)	It is defined as the average of the squared difference between the target value and the predicted value by the regression model. $\mathrm{MSE}=\frac{{{\displaystyle \sum }}_{i=0}^N{\left(y_i-y_i^p\right)}^2}{N}$
11	R2 (R-Squared)	It is defined as the statistical measure of fit that indicates how much total variation of a dependent variable is explained by the independent variable by the regression model. $R^2=1-\frac{Unexplained variation}{Total variation}$

Where TP—true positive; TN—true negative; FP—false positive; FN—false negative; $y_i$ and $y_i^p$ are the target variable and predicted values; N represents the total number of samples.