Prediction of Myopia in Adolescents through Machine Learning Methods

Abstract

According to literature, myopia has become the second most common eye disease in China, and the incidence of myopia is increasing year by year, and showing a trend of younger age. Previous researches have shown that the occurrence of myopia is mainly determined by poor eye habits, including reading and writing posture, eye length, and so on, and parents’ heredity. In order to better prevent myopia in adolescents, this paper studies the influence of related factors on myopia incidence in adolescents based on machine learning method. A feature selection method based on both univariate correlation analysis and multivariate correlation analysis is used to better construct a feature sub-set for model training. A method based on GBRT is provided to help fill in missing items in the original data. The prediction model is built based on SVM model. Data transformation has been used to improve the prediction accuracy. Results show that our method could achieve reasonable performance and accuracy.

A Study on the Design of Vision Protection Products Based on Children’s Visual Fatigue under Online Learning Scenarios

“…Existing studies have suggested that the prevalence of myopia in grade 3 children increased from 13.3% at the end of 2019 to 20.8% at the end of 2020 [4]. In addition, wrong eye posture and prolonged irregular eye use are also some of the important factors leading to vision loss [5][6][7]. Most studies in China and abroad regarding the impact of reading and writing postures on myopia have focused on examining the viewing distance, chest-table distance, viewing angle, and viewing duration.…”

Applications of Artificial Intelligence in Myopia: Current and Future Directions

“…Lin et al ( 45 ) identified myopia development rules and predicted the onset of myopia and its progression for children and teenagers from clinical measures using a random forest ML model, which had good predictive performance (the AUC ranged from 0.801 to 0.837) for up to 8 years in the future. Yang et al ( 46 ) developed a prediction model to predict myopia in adolescents based on both measurement and behavior data of primary school students, and the model achieved reasonable performance and accuracy. Further research is still required for interpopulation validation to allow these models to be generalized.…”

Artificial Intelligence to Reduce Ocular Health Disparities: Moving From Concept to Implementation

“…[23][24][25] As myopia continues to be a leading and growing cause of visual impairment globally, it remains to be seen how this proof of efficacy might translate into effective and scalable systems of care and how AI might play a role in the care pathway. 26 One particularly intriguing case of rapidly scaling technology is the smartphone-based mobile refractometer available via GoCheck Kids. 27,28 This cloud-base service provides point-of-care diagnosis of refractive error for use in the primary care setting and has created a datarich environment that has now used AI to improve end-user actions to capture the best image possible to improve the diagnostic accuracy.…”