Machine learning approach for unmet medical needs among middle-aged adults in South Korea: a cross-sectional study.

Journal: BMC health services research
PMID: 40361151

Abstract

BACKGROUND: South Korea is reported to have higher levels of unmet medical needs (UMN) than other countries, particularly among the middle-aged adult population. Considering that this group constitutes a substantial portion of the country's productive workforce, their health requires continuous management to ensure sustained productivity. The purpose of this study is to investigate the factors associated with UMN in economically active middle-aged adults and to develop a model to predict the occurrence of UMN.

Authors

  • Jeewuan Kim
    Department of Health Administration, Gyeonggi College of Science and Technology, Gyeonggi, South Korea.
  • Seok-Min Ji
    Department of Cancer AI and Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi, South Korea.
  • In-Sik Kim
    Department of Health Policy and Management, Korea University, Seoul, South Korea.
  • Ha-Young Jang
    Department of Health Policy and Management, Korea University, Seoul, South Korea.
  • Chang-Hyun Yoo
    Department of Health Policy and Management, Korea University, Seoul, South Korea.
  • Jae-Hak Kim
    Department of Fitness Promotion and Rehabilitation Exercise, National Rehabilitation Center, Seoul, South Korea.
  • Kyu-Min Kim
    Department of Health Administration, Gyeonggi College of Science and Technology, Gyeonggi, South Korea. perves@gtec.ac.kr.

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