| dc.description.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. | en_US |
