[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128017-en":3,"doc-seo-128017-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},128017,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Key risk factors of generalized anxiety disorder in adolescents: machine learning study","Generalized Anxiety Disorder (GAD) in adolescents is often underdiagnosed due to vague symptoms and delayed care, despite its substantial impact on social functioning and long-term well-being. This study analyzed Korea Youth Risk Behavior Web-based Survey (KYRBS) data from 2020 to 2023 to determine factors associated with GAD, applying machine learning methods including Lasso Regression, SelectKBest, and XGBoost for feature selection. Random Forest and Artificial Neural Networks were used for prediction, showing XGBoost’s feature selection to identify key variables and deliver strong model performance. Findings highlight sleep management, smoking prevention, and balanced nutrition as practical targets for early diagnosis and intervention.","TYPE Original Research PUBLISHED 07 January 2025  \nDOI 10.3389/fpubh.2024.1504739  \nOPEN ACCESS  \nEDITED BY  \nWulf Rössler,  \nCharité University Medicine Berlin, Germany  \nREVIEWED BY  \nFilipa Novais,  \nSanta Maria Hospital, Portugal Samuel Huang,  \nVirginia Commonwealth University, United States  \n*CORRESPONDENCE  \nHyekyung Woo  \n [hkwoo@kongju.ac.kr](hkwoo@kongju.ac.kr)  \nRECEIVED 01 October 2024  \nACCEPTED 18 December 2024  \nPUBLISHED 07 January 2025  \nCITATION  \nMoon Y and Woo H (2025) Key risk factors of generalized anxiety disorder in adolescents: machine learning study.  \nFront. Public Health 12:1504739.  \ndoi: 10.3389/fpubh.2024.1504739  \nCOPYRIGHT  \n© 2025 Moon and Woo. This is an  \nopen-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.  \nKey risk factors of generalized anxiety disorder in adolescents:  \nmachine learning study  \nYonghwan Moon 1 and Hyekyung Woo 1,2*  \n1 Department of Health Administration, Kongju National University of Nursing and Health, Kongju, Republic of Korea, 2 Institute of Health and Environment of Kongju National University, Kongju, Republic of Korea  \nAdolescents worldwide are increasingly affected by mental health disorders, with anxiety disorders, including Generalized Anxiety Disorder (GAD), being particularly prevalent. Despite its significant impact, GAD in adolescents often remains underdiagnosed due to vague symptoms and delayed medical attention, highlighting the need for early diagnosis and prevention strategies. This study utilized data from the Korea Youth Risk Behavior Web-based Survey (KYRBS) from 2020 to 2023 to analyze factors influencing GAD in adolescents. Using machine learning techniques such as Lasso Regression, SelectKBest, and XGBoost, we identified key variables, including health behaviors such as sleep, smoking, and fast-food intake, as significant factors associated with GAD. Predictive models using Random Forest and Artificial Neural Networks demonstrated that the XGBoost feature selection method effectively identified key factors and showed strong performance. These findings emphasize the need for educational programs focusing on sleep management, smoking prevention, and balanced nutrition to reduce the risk of GAD in adolescents, providing crucial insights for early diagnosis and intervention efforts.  \nKEYWORDS  \nadolescent, mental health, generalized anxiety disorder, machine learning, health behaviors  \n1 Introduction  \nAccording to mental health data from the World Health Organization (WHO), 14% of adolescents worldwide experience mental disorders ( 1) . Inadequate management of these disorders can lead to severe disruptions in social functioning, with serious consequences, such as suicide (2). Among mental disorders, anxiety disorders are particularly prevalent, affecting an estimated 3.6% of the individuals aged 10–14 years and 4.6% of those aged 15–19 years ( 1), a higher prevalence compared to attention deficit hyperactivity disorder and depression (3) . Generalized Anxiety Disorder (GAD), a subtype of anxiety disorders, often manifests during adolescence and is characterized by persistent worry and anxiety in daily life (4). According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, GAD is diagnosed when excessive anxiety or worry persists for at least 6 months, is difficult to control, and is accompanied by at least three symptoms, including restlessness, fatigue, difficulty concentrating, irritability, muscle tension, and sleep disturbances (5) .  \nDespite the increasing prevalence of GAD among adolescents, it remains relatively uncommon for individu","cbCaikI6q3Qaf3Gy","https://ap.wps.com/l/cbCaikI6q3Qaf3Gy","pdf",428759,1,9,"English","en",105,"# Introduction\n## Background and prevalence of adolescent GAD\n## Diagnostic characteristics and barriers to care\n## Prior risk factors and research gap\n## Machine learning in mental health research","[{\"question\":\"Which data source and time span were used to study risk factors for adolescent GAD?\",\"answer\":\"The study used the Korea Youth Risk Behavior Web-based Survey (KYRBS) data from 2020 to 2023 to analyze factors associated with GAD in adolescents.\"},{\"question\":\"How did the study identify key variables linked to GAD?\",\"answer\":\"It applied machine learning feature selection methods including Lasso Regression, SelectKBest, and XGBoost to determine significant health and behavioral variables.\"},{\"question\":\"What health behaviors were found as significant factors related to GAD risk?\",\"answer\":\"Key associated variables included sleep-related factors, smoking, and fast-food intake as significant factors associated with GAD.\"}]","Key risk factors of generalized anxiety disorder in adolescents: machine learning study | 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data source and time span were used to study risk factors for adolescent GAD?","Question",{"text":76,"@type":77},"The study used the Korea Youth Risk Behavior Web-based Survey (KYRBS) data from 2020 to 2023 to analyze factors associated with GAD in adolescents.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How did the study identify key variables linked to GAD?",{"text":81,"@type":77},"It applied machine learning feature selection methods including Lasso Regression, SelectKBest, and XGBoost to determine significant health and behavioral variables.",{"name":83,"@type":74,"acceptedAnswer":84},"What health behaviors were found as significant factors related to GAD risk?",{"text":85,"@type":77},"Key associated variables included sleep-related factors, smoking, and fast-food intake as significant factors associated with 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