Table 1.
Patient characteristics in the training and validation sets.
Table 2.
Predictive Performance of Different Machine Learning Models on Training and Test Sets.
Fig 1.
Variable importance analysis of the XGBoost model.
Pupil size (PS) is the most influential factor affecting axial elongation rate, with the highest importance score (100), followed by central corneal thickness (CCT, 40.88) and age of onset (17.96). In contrast, anterior chamber depth (ACD), corneal diameter (CD), and corneal curvature (CC, white to wthite, WTW) demonstrated relatively lower predictive contributions.