Prediction of adolescent weight status by machine learning: a population-based study.

Journal: BMC public health
PMID: 38769481

Abstract

BACKGROUND: Adolescent weight problems have become a growing public health concern, making early prediction of non-normal weight status crucial for effective prevention. However, few temporal prediction tools for adolescent four weight status have been developed. This study aimed to predict the short- and long-term weight status of Hong Kong adolescents and assess the importance of predictors.

Authors

  • Hengyan Liu
    School of Nursing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
  • Yik-Chung Wu
    Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
  • Pui Hing Chau
    School of Nursing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
  • Thomas Wai Hung Chung
    Family and Student Health Branch, Department of Health, Kwun Tong, Kowloon, Hong Kong SAR, China.
  • Daniel Yee Tak Fong
    School of Nursing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.

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