Robust identification key predictors of short- and long-term weight status in children and adolescents by machine learning.

Journal: Frontiers in public health
PMID: 39381765

Abstract

BACKGROUND: Early identification of high-risk individuals for weight problems in children and adolescents is crucial for implementing timely preventive measures. While machine learning (ML) techniques have shown promise in addressing this complex challenge with high-dimensional data, feature selection is vital for identifying the key predictors that can facilitate effective and targeted interventions. This study aims to utilize feature selection process to identify a robust and minimal set of predictors that can aid in the early prediction of short- and long-term weight problems in children and adolescents.

Authors

  • Hengyan Liu
    School of Nursing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
  • Yang Leng
    School of Electrical and Power Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou, Jiangsu 221116, 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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