Fig. 3

Calibration curves comparing the performance of all developed models. This graph provides a visual comparison of the calibration curves for each classifier developed 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 represents a model’s ability to estimate the probability of patient improvement after Fast Track (FT) surgery. The closer a curve follows the dashed line (which represents perfect calibration), the more accurately the model predicts the true outcomes. The Brier scores is provided for each as a measure of overall performance, with lower values corresponding to better performance