Exploring predictors of insomnia severity in shift workers using machine learning model.

Journal: Frontiers in public health
PMID: 40161021

Abstract

INTRODUCTION: Insomnia in shift workers has distinctive features due to circadian rhythm disruption caused by reversed or unstable sleep-wake cycle work schedules. While previous studies have primarily focused on a limited number of predictors for insomnia severity in shift workers, there is a need to further explore key predictors, and develop a data-driven prediction model for insomnia in shift workers. This study aims to identify potential predictors of insomnia severity in shift workers using a machine learning (ML) approach and evaluate the accuracy of the resulting prediction model.

Authors

  • Hyewon Yeo
    Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea.
  • Hyeyeon Jang
    Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea.
  • Nambeom Kim
    Department of Biomedical Engineering Research Center, Gachon University, Inchon, Republic of Korea.
  • Sehyun Jeon
    Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea.
  • Yunjee Hwang
    Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
  • Chang-Ki Kang
    Department of Radiological Science, College of Health Science, Gachon University, Incheon, Republic of Korea.
  • Seog Ju Kim
    Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea.

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