Data-Driven Study of Behavioral Predictors of Myopia: A Machine Learning Based Early Screening Approach

Abstract

Myopia is growing very fast, particularly in school-going children, primarily owing to lifestyle and educational transformations of digital technology. The research is an evaluation of machine learning models with regards to early and non-clinical prediction of childhood myopia based on behavioral and environmental factors. The 1,002 participants provided a survey of daily screen time, outdoor activity, sleep time, the distance of visiting reading materials, posture and the history of ocular health of parents. Designed to be trained and evaluated using standard metrics of evaluation, the four models, namely: Logistic Regression, Random Forest, Gradient Boosting and a two-layer Artificial Neural Network (ANN) were trained and evaluated. Artificial Neural Network had the largest AUC (0.829) and ANN showed the highest sensitivity with a recall of 96.1, which is why it will be highly suitable in screening settings where false negativity is the major issue. As identified during feature-importance analyses, outdoor exposure, screen-time and duration of sleep were the most significant factors in relation to a risk factor of myopia. Altogether, the results suggest that behavioral data may be successfully used as the support of lightweight and low-cost myopia risk assessment tools, which may be helpful to schools, their parents and primary-care settings in search of the early detection and preventive interventions.