Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents

This study aimed to automatically classify physical fitness and cardiometabolic risk in a Chilean adolescent using self-organizing maps. This cross-sectional study analysed a nationally representative database from the Physical Education Quality Measurement System (n = 7197). Physical fitness and cardiometabolic risk variables were derived from anthropometric indicators. Self-Organizing maps (SOM) were employed to identify participant profiles based on an unsupervised predictive model. After implementing and training the SOM, a detailed analysis of the generated maps was conducted to interpret the revealed relationships and clusters. The analysis resulted in three classification groups, categorizing the sample into low, moderate, and high-risk levels. Students with better physical fitness exhibited lower cardiometabolic risk levels and a lower body mass index. SOM, through an unsupervised model, is a reliable tool for classifying cardiometabolic risk and physical fitness in adolescents. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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Indexación: Scopus

Keywords

Big Data, Exercise, Health, Machine Learning

Citation

International Journal of Adolescence and Youth. Volume 29, Issue 1. 2024. Article number 2417903

DOI

10.1080/02673843.2024.2417903

URI

https://repositorio.unab.cl/handle/ria/63270

Collections

FEd - Artículos de Revista

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