Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents
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Fecha
2024
Autores
Yáñez-Sepúlveda, Rodrigo
Olivares, Rodrigo
Ravelo, Camilo
Cortés-Roco, Guillermo
Zavala-Crichton, Juan Pablo
Hinojosa-Torres, Claudio
de Souza-Lima, Josivaldo
Monsalves-Álvarez, Matías
Reyes-Amigo, Tomás
Hurtado-Almonacid, Juan
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
Routledge
Nombre de Curso
Licencia CC
Attribution-NonCommercial 4.0 International Deed (CC BY-NC 4.0)
Licencia CC
https://creativecommons.org/licenses/by-nc/4.0/
Resumen
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.
Notas
Indexación: Scopus
Palabras clave
Big Data, Exercise, Health, Machine Learning
Citación
International Journal of Adolescence and Youth. Volume 29, Issue 1. 2024. Article number 2417903
DOI
10.1080/02673843.2024.2417903