Key risk factors of generalized anxiety disorder in adolescents: machine learning study
Adolescents 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 at...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1504739/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841556883369885696 |
|---|---|
| author | Yonghwan Moon Hyekyung Woo Hyekyung Woo |
| author_facet | Yonghwan Moon Hyekyung Woo Hyekyung Woo |
| author_sort | Yonghwan Moon |
| collection | DOAJ |
| description | Adolescents 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. |
| format | Article |
| id | doaj-art-a0dbb8feb1224af59c3d28e7fc5af3af |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-a0dbb8feb1224af59c3d28e7fc5af3af2025-01-07T05:23:49ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011210.3389/fpubh.2024.15047391504739Key risk factors of generalized anxiety disorder in adolescents: machine learning studyYonghwan Moon0Hyekyung Woo1Hyekyung Woo2Department of Health Administration, Kongju National University of Nursing and Health, Kongju, Republic of KoreaDepartment of Health Administration, Kongju National University of Nursing and Health, Kongju, Republic of KoreaInstitute of Health and Environment of Kongju National University, Kongju, Republic of KoreaAdolescents 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.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1504739/fulladolescentmental healthgeneralized anxiety disordermachine learninghealth behaviors |
| spellingShingle | Yonghwan Moon Hyekyung Woo Hyekyung Woo Key risk factors of generalized anxiety disorder in adolescents: machine learning study Frontiers in Public Health adolescent mental health generalized anxiety disorder machine learning health behaviors |
| title | Key risk factors of generalized anxiety disorder in adolescents: machine learning study |
| title_full | Key risk factors of generalized anxiety disorder in adolescents: machine learning study |
| title_fullStr | Key risk factors of generalized anxiety disorder in adolescents: machine learning study |
| title_full_unstemmed | Key risk factors of generalized anxiety disorder in adolescents: machine learning study |
| title_short | Key risk factors of generalized anxiety disorder in adolescents: machine learning study |
| title_sort | key risk factors of generalized anxiety disorder in adolescents machine learning study |
| topic | adolescent mental health generalized anxiety disorder machine learning health behaviors |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1504739/full |
| work_keys_str_mv | AT yonghwanmoon keyriskfactorsofgeneralizedanxietydisorderinadolescentsmachinelearningstudy AT hyekyungwoo keyriskfactorsofgeneralizedanxietydisorderinadolescentsmachinelearningstudy AT hyekyungwoo keyriskfactorsofgeneralizedanxietydisorderinadolescentsmachinelearningstudy |