Near viewing behaviors predict educational system in a machine learning model.

Intensive education systems are believed to contribute to high rates of myopia. This study examined whether near-viewing behaviors in college students differ based on their pre-college educational systems and whether these behaviors can be used to classify students' educational background using machine learning. Male students ages 18-33 years who attended either an intensive (ultra-Orthodox) or a standard school system (non-ultra-Orthodox) prior to college were recruited. Refractive error was measured and near-viewing behaviors were assessed using a wearable sensor during academic study periods. Compared to standard school students, intensive school students had significantly more myopic refraction (P < 0.03), spent more time viewing very near distances (P < 0.004) and less time viewing intermediate distances (P < 0.008) and had shorter near-viewing distances (P < 0.0001). Machine learning identified far-viewing episodes > 5 min and viewing distance during near-viewing as predictors of educational background.These findings suggest that educational environments are associated with distinct visual behavior patterns that may be linked to refractive development. The ability to use machine learning to predict educational systems based solely on near-viewing behaviors underscores its potential as a tool for investigating educational and behavioral factors and refractive outcomes.