Fig. 3

a) Confusion matrices for a detailed summary of the ANN’s performance trained with the transformed features from Principal Component Analysis (PCA). 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 - 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 example, considering the training dataset, in the row corresponding to the “HV” class, the percentage of HV correctly classified is shown along with the percentage of HV misclassified as having MO (TN = 68%; FP = 32%). Similarly, for the “MO” class, the row summary indicates the percentage of individuals correctly identified as having MO (TP = 71%) and those misclassified as HV (FN = 29%). b) Area under the curve (AUC) for the overall performance of the ANN trained with the transformed features from PCA. The ROC curve is a graphical representation that illustrates the performance of a binary classifier across different threshold settings. The ROC curve plots the True Positive Rate (TPR) on the y-axis against the False Positive Rate (FPR) on the x-axis for all possible threshold values. As the threshold varies, the trade-off between correctly classifying positive cases and misclassifying negative cases as positive is visualized. An ideal classifier would achieve a point near the top-left corner of the ROC plot, where the TPR is high, and the FPR is low. Furthermore, the AUC provides a single metric that summarizes the classifier’s overall performance. An AUC of 1 represents perfect classification, where the classifier achieves a high TPR with a low FPR across all thresholds. An AUC of 0.5, however, indicates performance no better than random chance