Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models

Classification models, particularly gradient boosting and random forest, demonstrated strong potential for predicting clinically significant vault categories, enabling personalized surgical planning and improved risk management. While regression models offered moderate insights, their limitations suggest that classification approaches are better suited for clinical applications. Future research should focus on enhancing model accuracy for extreme vault prediction and integrating advanced techniques, such as ensemble deep learning, to further refine outcomes.