Dataset for: Predicting Patient Adaptation to Progressive Addition Lenses: A Machine Learning Framework for Personalized Dispensing

This dataset supports the research paper titled "Predicting Patient Adaptation to Progressive Addition Lenses: A Machine Learning Framework for Personalized Dispensing." It contains anonymized data from electronic health records used to train and validate machine learning models (Logistic Regression, Random Forest, and XGBoost) for predicting non-adaptation in first-time Progressive Addition Lens (PAL) wearers. The study identifies key physiological predictors such as Vertical Phoria and Cylinder Power using SHAP (SHapley Additive exPlanations) analysis. Contents:- Demographic data (Age, etc.)- Refractive data (Cylinder, Axis, Add Power)- Binocular vision metrics (Vertical Phoria)- Lens design parameters- Adaptation outcomes (Success/Failure)