[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122109-en":3,"doc-seo-122109-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122109,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine-learning models to predict myopia in children and adolescents","Machine-learning applications are evaluated for myopia prediction and the identification of key influencing factors among children and adolescents. A case-control study using stratified cluster random sampling enrolled 2,947 elementary school students in Shenzhen, China, combining myopia screening, ocular biological measurements, and questionnaires. Five algorithms (RF, DT, XGBoost, SVM, and LR) were compared with receiver operating characteristic performance and variable importance analyses. All models achieved AUC > 0.75, supporting risk-factor–guided prevention strategies.","OPEN ACCESS  \nEDITED BY  \nXinyu Liu,  \nSingapore Eye Research Institute (SERI), Singapore  \nREVIEWED BY  \nZhengcun Pei,  \nTianjin University, China Bhim Bahadur Rai,  \nAustralian National University, Australia  \n*CORRESPONDENCE  \nJingfeng Mu  \n [1014120300@qq.com](1014120300@qq.com)  \n†These authors have contributed equally to this work  \nRECEIVED 04 September 2024  \nACCEPTED 07 November 2024  \nPUBLISHED 19 November 2024  \nCITATION  \nMu J, Zhong H and Jiang M (2024)  \nMachine-learning models to predict myopia in children and adolescents.  \nFront. Med. 11:1482788 .  \ndoi: 10.3389/fmed.2024.1482788  \nCOPYRIGHT  \n© 2024 Mu, Zhong and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTYPE Original Research PUBLISHED 19 November 2024 DOI 10.3389/fmed.2024.1482788  \nMachine-learning models to predict myopia in children and adolescents  \nJingfeng Mu *†, Haoxi Zhong† and Mingjie Jiang Shenzhen Eye Hospital, Shenzhen, China  \nObjectives: To explore machine-learning applications in myopia prediction and analyze the influencing factors of myopia.  \nMethods: Stratified cluster random sampling was used to select elementary school students in Shenzhen, China for inclusion in this case-control study. Myopia screening, ocular biological parameter measurements, and questionnaires were conducted. Random forest (RF), decision tree (DT), extreme gradient boosting trees (XGBoost), support vector machine (SVM), and logistic regression (LR) algorithms were used to construct five myopia prediction models using R software (version 4.3.0) . These myopia prediction models were used to investigate the relationship between ocular biological parameters, environmental factors, behavioral factors, genetic factors, and myopia.  \nResults: This study included 2,947 elementary school students, with a myopia prevalence rate of 47. 2% . All five prediction models had an area under the receiver operating characteristic curve (AUC) above 0.75, with prediction accuracy and precision exceeding 0.70. The AUCs in the testing set were 0. 846, 0. 837, 0. 833, and 0.815 for SVM, LR, RF, and XGBoost, respectively, indicating their superior predictive performance to that of DT (0 .791) . In the RF model, the five most important variables were axial length, age, sex, maternal myopia, and feeding pattern. LR identified axial length was the most significant risk factor for myopia [odds ratio (OR) =8 . 203], followed by sex (OR = 2.349), maternal myopia (OR = 1.437), Reading and writing posture (OR = 1. 270), infant feeding pattern (OR = 1. 207), and age (OR = 1. 168); corneal radius (OR = 0.034) and anterior chamber depth (OR = 0. 516) served as protective factors.  \nConclusion: Myopia prediction models based on machine learning demonstrated favorable predictive performance and accurately identified myopia risk factors, and may therefore aid in the implementation of myopia prevention and control measures among high-risk individuals.  \nKEYWORDS  \nmachine learning, myopia, influencing factors, children and adolescents, predictive model  \nIntroduction  \nMyopia is a global public health concern ( 1) that affected 1.4 billion individuals worldwide in 2020 with a prevalence of 22.9%. It has been projected that this number will increase to 4.7 billion individuals by 2050, resulting in a prevalence of 49.8%(2) . In 2020, 52.7% of children and adolescents in China were myopic (3), which contributed to the position of China as the country with the highest number of individuals with myopia (4) . Myopia often develops during childhood and adolescence, and myopia development during this pe","cbCaihhdOAoXN7fy","https://ap.wps.com/l/cbCaihhdOAoXN7fy","pdf",1281507,1,10,"English","en",105,"# Objectives\n# Methods\n## Sampling and participants\n## Measurements and algorithms\n# Results\n## Model performance\n## Key variables and risk factors\n# Conclusion\n# Keywords","[{\"question\":\"What is the main objective of the study on myopia in children and adolescents?\",\"answer\":\"To apply machine learning for myopia prediction and analyze factors influencing myopia development.\"},{\"question\":\"How were participants selected and what data were used to build prediction models?\",\"answer\":\"Elementary school students in Shenzhen were selected using stratified cluster random sampling, with myopia screening, ocular biological parameter measurements, and questionnaires.\"},{\"question\":\"Which machine-learning models performed best and what evidence supports their predictive ability?\",\"answer\":\"SVM, LR, RF, and XGBoost showed testing-set AUC values above 0.75 and accuracy/precision above 0.70, indicating superior performance compared with DT.\"}]","Machine-learning models to predict myopia in children and adolescents | 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