Exploring predictors of insomnia severity in shift workers using machine learning model
This ML algorithm provides an effective method for identifying key factors that predict insomnia severity in shift workers. Our findings align with the traditional insomnia model while also reflecting the distinctive features of shift work such as workplace conditions. Although the potential for immediate clinical application is limited, this study can serve as guidance for future research in improving a prediction model for shift workers. Constructing comprehensive ML-based prediction models that include our key predictors could be a crucial approach for clinical purposes.
Authors
- Hyewon Yeo (ORCID: https://orcid.org/0000-0002-4245-6933)
- Seog Ju Kim (ORCID: https://orcid.org/0000-0003-2467-5451)
- Hyeyeon Jang (ORCID: https://orcid.org/0000-0001-5709-6240)
- Chang‐Ki Kang (ORCID: https://orcid.org/0000-0002-9573-8432)
- Yunjee Hwang (ORCID: https://orcid.org/0000-0001-7246-9316)
- Nambeom Kim (ORCID: https://orcid.org/0000-0003-4960-1256)
- Sehyun Jeon (ORCID: https://orcid.org/0000-0002-6594-6556)
Institutions
- Gachon University (KR)
- Korea University (KR)
- Samsung Medical Center (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2025-03-14
- DOI
- https://doi.org/10.3389/fpubh.2025.1494583
- Citations
- 1
- Primary Topic
- Sleep and Work-Related Fatigue
- Type
- article
- Field-Weighted Citation Impact
- 2.12