Machine Learning-Based Prediction of Postoperative Refraction in Cataract Surgery: A Stacking Ensemble Approach

Background: Achieving precise postoperative refractive outcomes remains a significant challenge in cataract surgery. While advanced intraocular lens (IOL) power calculation formulas exist, they are constrained by their singular algorithmic structures. This study investigated whether a stacking ensemble machine learning approach could overcome these limitations. Methods: A dataset of 1,710 eyes from patients who underwent cataract surgery with monofocal IOL implantation (Vivinex or SA60AT) was utilized. Following rigorous preprocessing and feature engineering, a stacking ensemble architecture was developed comprising three diverse base learners (Multi-Layer Perceptron, Support Vector Regressor with RBF kernel, and SplineTransformer with Linear Regression) and a Ridge Regressor meta-learner. The model was trained on 80% of the data using 5-fold cross-validation and evaluated on an independent 20% test set (n=341). Performance was compared against six standard IOL formulas. Results: The stacking ensemble model demonstrated excellent predictive accuracy, achieving a Mean Absolute Error (MAE) of 0.272 D on the independent test set (n=341). The model achieved lower MAE compared to all six standard IOL formulas, including Kane (MAE 0.295 D) and Barrett Universal II (MAE 0.318 D). Clinically, 85.1% of eyes achieved predictions within +/-0.50 D, compared to 82.5% for Kane formula and 81.8% for Barrett Universal II. Conclusion: The stacking ensemble machine learning model significantly enhances postoperative refraction prediction accuracy compared to established IOL calculation formulas. By leveraging algorithmic diversity and data-driven learning, this approach represents a promising advancement toward reducing refractive surprises and improving patient satisfaction in cataract surgery. External validation on independent datasets is required to confirm generalizability.