Development and verification of prediction model for influencing factors of myopia among primary and middle school students based on machine learning
10.3980/j.issn.1672-5123.2025.2.29
- VernacularTitle:基于机器学习的中小学生近视影响因素分析及预测模型的开发与验证
- Author:
Xiaocheng GU
1
,
2
;
Xinli CHEN
1
,
2
;
Jian CHEN
1
,
2
;
Cong MENG
1
,
2
;
Haiping DUAN
1
,
2
Author Information
1. School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong Province, China
2. Center for Disease Control and Prevention of Shinan District, Qingdao 266071, Shandong Province, China
- Publication Type:Journal Article
- Keywords:
primary and secondary school students;
myopia;
influencing factors;
machine learning;
prediction model
- From:
International Eye Science
2025;25(2):328-336
- CountryChina
- Language:Chinese
-
Abstract:
AIM: To screen and analyze the influencing factors of myopia among primary and secondary school students and establish a predictive model to provide ideas for the prevention and control measures of myopia among children and adolescents.METHODS:A total of 1 759 primary and secondary school students from 2 primary schools, 2 junior high schools, 2 senior high schools and 1 vocational high school in the urban area of Qingdao were sampled by means of stratified cluster sampling in September 2023. Vision screening and a questionnaire survey on influencing factors were carried out based on machine learning algorithms. The screening and determination were mainly conducted in accordance with the Standard Logarithmic Visual Acuity Chart(GB/T11533-2011)and the Specifications for Screening Myopia in Children and Adolescents. The influencing factors of myopia were analyzed and a prediction model was developed based on the machine learning algorithms LASSO in combination with XGBoost, and visualization was achieved through an interactive Nomogram. Statistical analysis was performed using R statistical software version 4.3.3.RESULTS:The screening prevalence of myopia among primary and secondary school students in the urban area of Qingdao was 70.61%(1 242 cases). The optimal predictive variables for screening were grade, gender, whether parents were myopic, daily indoor sedentary time, appropriate distance between eyes and books during reading and writing, daily sleep time, distance between eyes and TV screen when watching TV/playing video games exceeding 3 meters, the playground during breaks, total duration of tutorial classes, how often eyes are rested during near work, daily computer usage time, and average daily homework time after school, totaling 12 influencing factors. The AUCs of the training set and test set were 0.770(95%CI:0.751-0.789)and 0.732(95%CI:0.714-0.750), respectively.CONCLUSION: A machine learning-based prediction model was developed and validated to predict the risk of myopia onset in primary and secondary school students, accompanied by effective visualization techniques.
