For both validations, the following metrics were calculated with Pycaret: Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 score, Kappa, Matthew's Correlation Coefficient (MCC) and Balanced Accuracy (BA). All seven metrics are statistical indicators of quality, used for model evaluation, offering insight into prediction accuracy with a focus on the active class. For both validations, we obtained the MCC and calculated its average for all models, so as to select the three best architectures for the data sets studied in this analysis (Table ). MCC is a robust metric for assessing the quality of binary classification models, ranging from -1 to 1. A value of 1 indicates perfect classification, 0 corresponds to random predictions, and -1 represents completely inverse predictions. The architectures selected for modeling were retrained on the complete data set corresponding to each target, aiming to significantly enhance the MCC and improve model generalization by optimizing performance.