Figure 2.

Preprocessing of numerical features using a standard scaler with z-score normalization. The left plot shows the original distribution of feature values for a sample numerical feature (i.e., central retinal thickness) across patient sessions. The right plot displays the same feature after normalization using a standard scaler: the data distribution is transformed to have a mean of 0 and a variance of 1. In both plots, the red solid line represents the mean, while the black dashed lines indicate one standard deviation above and below the mean. Standard scaling was applied to all numerical predictors to ensure consistency in model training.