Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors
The XGBM model may be a useful tool to estimate the risk of experiencing severe sleep disturbance among adolescents.
Authors
- Zhongbing Yang
- Zheng Hua (ORCID: https://orcid.org/0000-0002-4925-6627)
- Shaocong Zhao
- Mingxing Lei (ORCID: https://orcid.org/0000-0002-9769-6977)
- Zhang Li-rong (ORCID: https://orcid.org/0000-0002-7316-4415)
- Zhenbo Wu (ORCID: https://orcid.org/0000-0003-2894-2617)
- Wei Yang (ORCID: https://orcid.org/0009-0005-8713-6262)
Institutions
- Chongqing Normal University (CN)
- Guizhou Normal University (CN)
- Chinese PLA General Hospital (CN)
- Guangdong University of Education (CN)
- Hainan General Hospital (CN)
- PLA Academy of Military Science (CN)
- Xiamen University of Technology (CN)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2024-11-07
- DOI
- https://doi.org/10.3389/fpsyt.2024.1447281
- Citations
- 5
- Primary Topic
- Sleep and related disorders
- Type
- article
- Field-Weighted Citation Impact
- 2.54
Funders
- Xiamen University of Technology
- Xiamen University