Fig. 5

a) Confusion matrices for a detailed summary of the ANN’s performance trained with the Forward Feature Selection (FFS). The matrix is structured such that the rows represent the true classes (HV or MO), and the columns represent the predicted classes. The diagonal elements (in green) reflect the number of correctly classified cases (true negatives and true positives: TN and TP), while the off-diagonal elements (in red) capture the number of misclassifications false negatives and false positives: FN - FP). A row summary displays the percentages of correctly and incorrectly classified observations for each true class. For instance, considering the training dataset, in the “HV” class row, the percentage of correctly classified HV is displayed alongside the percentage misclassified as having MO (TN = 75%; FP = 25%). Similarly, for the “MO” class, the row summary reflects the percentage of individuals correctly identified as having MO (TP = 72%), and those misclassified as HV (FN = 28%). b) Area under the curve (AUC) for the overall performance of the ANN trained with the features selected with the FFS