Development and validation of a machine learning model for predicting pediatric metabolic syndrome using anthropometric and bioelectrical impedance parameters.

Journal: International journal of obesity (2005)
Published Date: Apr 1, 2025

Abstract

OBJECTIVE: Metabolic syndrome (MS) is a risk factor for cardiovascular diseases, and its prevalence is increasing among children and adolescents. This study developed a machine learning model to predict MS using anthropometric and bioelectrical impedance analysis (BIA) parameters, highlighting its ability to handle complex, nonlinear variable relationships more effectively than traditional methods such as logistic regression.

Authors

  • Youngha Choi
    Department of Pediatrics, Kangwon National University Hospital, Chuncheon, Korea.
  • Kanghyuck Lee
    Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea.
  • Eun Gyung Seol
    Department of Pediatrics, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si, Republic of Korea.
  • Joon Young Kim
    Veterinary Medical Teaching Hospital, Konkuk University, Seoul, 05029, Republic of Korea.
  • Eun Byoul Lee
    Department of Pediatrics, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si, Republic of Korea.
  • Hyun Wook Chae
    Department of Pediatrics, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
  • Taehoon Ko
    Office of Hospital Information, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
  • Kyungchul Song
    Department of Pediatrics, Severance Children's Hospital, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, Korea.

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