[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118941-en":3,"doc-seo-118941-105":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},118941,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Pediatric obstructive sleep apnea diagnosis - leveraging machine learning with linear discriminant analysis","Pediatric obstructive sleep apnea diagnosis evaluates a machine learning approach for identifying OSA in children using clinical features obtainable in nonnocturnal, nonmedical settings, rather than relying solely on overnight polysomnography. The study collected data at Beijing Children’s Hospital from April 2018 to October 2019 from 2464 suspected children aged 3–18, using an 8:2 training/testing split and elastic net feature selection. External testing showed strong discrimination with linear discriminant analysis, offering a feasible screening alternative for stratifying disease severity.","TYPE Original Research PUBLISHED 14 February 2024 DOI 10.3389/fped.2024.1328209  \nEDITED BY  \nGonzalo C. Gutiérrez-Tobal, University of Valladolid, Spain  \nREVIEWED BY  \nDaniela Ferreira-Santos, University of Porto, Portugal Antonino Maniaci,  \nKore University of Enna, Italy  \n*CORRESPONDENCE  \nXin Ni  \n [nixin@bch.com.cn](nixin@bch.com.cn)[ ](nixin@bch.com.cn)Qing Wang  \n [8010020958@189.cn](8010020958@189.cn)[ ](8010020958@189.cn)Jun Tai  \n [trenttj@163.com](trenttj@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 26 October 2023  \nACCEPTED 30 January 2024  \nPUBLISHED 14 February 2024  \nCITATION  \nQin H, Zhang L, Li X, Xu Z, Zhang J, Wang S, Zheng L, Ji T, Mei L, Kong Y, Jia X, Lei Y, Qi Y, Ji J, Ni X, Wang Q and Tai J (2024) Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis.  \nFront. Pediatr. 12:1328209 .  \ndoi: 10.3389/fped.2024.1328209  \nCOPYRIGHT  \n© 2024 Qin, Zhang, Li, Xu, Zhang, Wang, Zheng, Ji, Mei, Kong, Jia, Lei, Qi, Ji, Ni, Wang and Tai. 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.  \nPediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis  \nHan Qin1†, Liping Zhang2†, Xiaodan Li3†, Zhifei Xu4, Jie Zhang3, Shengcai Wang3, Li Zheng3, Tingting Ji3, Lin Mei3, Yaru Kong1, Xinbei Jia1, Yi Lei5, Yuwei Qi6, Jie Ji3, Xin Ni3*, Qing Wang2,7* and Jun Tai6*  \n1Department of Child Health Care, Children’s Hospital Capital Institute of Pediatrics, Chinese Academy of Medical Sciences & Peking Union Medical College, Capital Institute of Pediatrics, Beijing, China, 2Pharmacovigilance Research Center for Information Technology and Data Science, Cross-strait Tsinghua Research Institute, Xiamen, China, 3Department of Otolaryngology, Head and Neck Surgery, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China, 4 Respiratory Department, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China, 5Faculty of Information Technology, Beijing University of Technology, Beijing, China, 6Department of Otolaryngology, Head and Neck Surgery, Children’s Hospital Capital Institute of Pediatrics, Beijing, China, 7Department of Automation, Tsinghua University, Beijing, China  \nObjective: The objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments. Patients and methods: This study was conducted at Beijing Children’s Hospital from April 2018 to October 2019 . The participants in this study were 2464 children aged 3–18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG) . Participants’ data were randomly divided into a training set and a testing set at a ratio of 8:2 . The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results.  \nResults: Feature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0 .73 for AHI ≥5 and 0 .78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternativ","cbCaisSVL7bwJuk0","https://ap.wps.com/l/cbCaisSVL7bwJuk0","pdf",2893156,1,9,"English","en",105,"# 1 Introduction\n# 2 Patients and Methods\n## 2.1 Study design and participants\n## 2.2 Feature selection and model training\n# 3 Results\n## 3.1 Selected features and classification performance\n## 3.2 External validation and comparison\n# 4 Discussion\n## 4.1 Clinical implications and limitations","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To evaluate how effectively a machine learning algorithm can diagnose pediatric OSA using clinical features that can be obtained outside nocturnal, medical environments.\"},{\"question\":\"How were the participants selected and split for model development?\",\"answer\":\"The study enrolled 2464 children aged 3–18 suspected of having OSA, and randomly divided their data into a training set and a testing set in an 8:2 ratio.\"},{\"question\":\"How does the model’s performance compare with polysomnography-based questionnaire features?\",\"answer\":\"Using features selected by elastic net, the machine learning model achieved external AUC values around 0.73 for AHI ≥5 and 0.78 for AHI ≥10, outperforming models based on PSG questionnaire features.\"}]","Pediatric obstructive sleep apnea diagnosis - leveraging machine learning with linear discriminant analysis | PDF",1785721119,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"pediatric-obstructive-sleep-apnea-diagnosis-leveraging-machine-learning-with-linear-discriminant-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/pediatric-obstructive-sleep-apnea-diagnosis-leveraging-machine-learning-with-linear-discriminant-analysis/118941/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this study?","Question",{"text":75,"@type":76},"To evaluate how effectively a machine learning algorithm can diagnose pediatric OSA using clinical features that can be obtained outside nocturnal, medical environments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the participants selected and split for model development?",{"text":80,"@type":76},"The study enrolled 2464 children aged 3–18 suspected of having OSA, and randomly divided their data into a training set and a testing set in an 8:2 ratio.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the model’s performance compare with polysomnography-based questionnaire features?",{"text":84,"@type":76},"Using features selected by elastic net, the machine learning model achieved external AUC values around 0.73 for AHI ≥5 and 0.78 for AHI ≥10, outperforming models based on PSG questionnaire features.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]