Fig. 1

ROC Curves comparing the performance of all developed models. This graph provides a visual comparison of the Receiver Operating Characteristic (ROC) curves for each classifier model established in our analysis: Hierarchical Shrinked Trees (HST), Fast Interpretable Greedy Sums (FIGS), Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), eXtreme Gradient Boosting (XGB), Support Vector Machines (SVM) and Multi-layer Perceptron (MLP). Each line traces the trade-off between sensitivity (true positive rate) and 1-specificity (false positive rate) across different thresholds. The Area Under the ROC Curve (AUC) is provided for each model as a measure of overall performance