Uncovering predictors of myopia in youth: a secondary data analysis using a machine learning approach

Our analysis confirms the multifactorial nature of myopia, blending historical biological mechanisms with contemporary behavioral drivers. The study demonstrates a scalable strategy for global myopia risk prediction by adaptively integrating diverse datasets. While not yet a turnkey clinical tool, this work lays the groundwork for future multimodal risk-prediction frameworks that can bridge era-specific biases and harness machine learning to capture the evolving profile of myopia risk.