Predicting hyperopic reserve in children based on school-based vision programs with ensemble learning.

Purpose: To develop a predictive model based on school-based vision screening programs for evaluating hyperopic reserve status in children aged 5-10 years. Methods: This cross-sectional study included 8,035 students aged 5-10 years from senior kindergarten and primary school in Guangdong Province, China. Participants underwent ocular biometric measurements, autorefraction, and questionnaire assessment before cycloplegia, after which autorefraction was repeated. An ensemble learning model was constructed to predict low hyperopic reserve (LHR), a refractive status below age-appropriate norms but not yet myopic, using NCR ocular parameters and questionnaire data. Model performance was evaluated and compared through nested 5-fold cross-validation. Sensitivity and robustness analyses were conducted to verify the comprehensive model performance. SHapley Additive exPlanations (SHAP) was utilized to interpret feature contributions and model logic. Results: On the test set, the ensemble learning model achieved an area under the curve (AUC) of 0.807, an accuracy of 0.737, and a precision of 0.747, outperforming all the single machine learning models. Compared to the direct NCR-based assessment and the +0.50 D / +0.75 D fixed-offset benchmarks (accuracy = 0.614-0.669, precision = 0.593-0.683), the ensemble learning model improved accuracy by 0.068-0.123 and precision by 0.064-0.154. SHAP analysis identified axial length to corneal radius ratio (SHAP value = 0.652), spherical equivalent (SHAP value = 0.514), and school type (SHAP value = 0.312) as the most three important predictors. Conclusions and Relevance: The proposed ensemble learning model offers a noncycloplegic, convenient approach for school-based vision programs to predict LHR in children aged 5-10 years. This model and its risk-assessment tool may support existing school-based vision programs in preserving children's hyperopic reserve.