School of Public Health, Jiamusi University, Jiamusi, Heilongjiang, China.
Clinical College of Anhui Medical University, Hefei, Anhui, China.
Front Public Health. 2023 Jun 1;11:1169128. doi: 10.3389/fpubh.2023.1169128. eCollection 2023.
We aim to develop myopia classification models based on machine learning algorithms for each schooling period, and further analyze the similarities and differences in the factors influencing myopia in each school period based on each model.
Retrospective cross-sectional study.
We collected visual acuity, behavioral, environmental, and genetic data from 7,472 students in 21 primary and secondary schools (grades 1-12) in Jiamusi, Heilongjiang Province, using visual acuity screening and questionnaires.
Machine learning algorithms were used to construct myopia classification models for students at the whole schooling period, primary school, junior high school, and senior high school period, and to rank the importance of features in each model.
The main influencing factors for students differ by school section, The optimal machine learning model for the whole schooling period was Random Forest (AUC = 0.752), with the top three influencing factors being age, myopic grade of the mother, and Whether myopia requires glasses. The optimal model for the primary school period was a Random Forest (AUC = 0.710), with the top three influences being the myopic grade of the mother, age, and extracurricular tutorials weekly. The Junior high school period was an Support Vector Machine (SVM; AUC = 0.672), and the top three influencing factors were gender, extracurricular tutorial subjects weekly, and whether can you do the "three ones" when reading and writing. The senior high school period was an XGboost (AUC = 0.722), and the top three influencing factors were the need for spectacles for myopia, average daily time spent outdoors, and the myopic grade of the mother.
Factors such as genetics and eye use behavior all play an essential role in students' myopia, but there are differences between school periods, with those in the lower levels focusing on genetics and those in the higher levels focusing on behavior, but both play an essential role in myopia.
我们旨在基于机器学习算法为每个学习阶段开发近视分类模型，并进一步根据每个模型分析各阶段影响近视的因素的相似性和差异性。
回顾性横断面研究。
我们从黑龙江省佳木斯市 21 所中小学（1-12 年级）的 7472 名学生中收集了视力、行为、环境和遗传数据，使用视力筛查和问卷调查。
使用机器学习算法构建了整个学习阶段、小学、初中和高中阶段学生的近视分类模型，并对每个模型中的特征重要性进行了排序。
不同学习阶段的学生主要影响因素不同。整个学习阶段的最佳机器学习模型是随机森林（AUC=0.752），前三个主要影响因素是年龄、母亲的近视程度和近视是否需要配镜。小学阶段的最佳模型是随机森林（AUC=0.710），前三个主要影响因素是母亲的近视程度、年龄和每周课外辅导班。初中阶段是支持向量机（SVM；AUC=0.672），前三个主要影响因素是性别、每周课外辅导班科目和读写时能否“三个一”。高中阶段是 XGBoost（AUC=0.722），前三个主要影响因素是近视配镜需求、平均每天户外活动时间和母亲的近视程度。
遗传和用眼行为等因素对学生近视都有重要作用，但各阶段存在差异，低阶段注重遗传，高阶段注重行为，但两者对近视都有重要作用。
