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

Vista sencilla de ítem
dc.contributor.authorYáñez-Sepúlveda, Rodrigo
dc.contributor.authorOlivares, Rodrigo
dc.contributor.authorRavelo, Camilo
dc.contributor.authorCortés-Roco, Guillermo
dc.contributor.authorZavala-Crichton, Juan Pablo
dc.contributor.authorHinojosa-Torres, Claudio
dc.contributor.authorde Souza-Lima, Josivaldo
dc.contributor.authorMonsalves-Álvarez, Matías
dc.contributor.authorReyes-Amigo, Tomás
dc.contributor.authorHurtado-Almonacid, Juan
dc.contributor.authorPáez-Herrera, Jacqueline
dc.contributor.authorMahecha-Matsudo, Sandra
dc.contributor.authorOlivares-Arancibia, Jorge
dc.contributor.authorClemente-Suárez, Vicente Javier
dc.date.accessioned2025-01-24T16:44:53Z
dc.date.available2025-01-24T16:44:53Z
dc.date.issued2024
dc.descriptionIndexación: Scopus
dc.description.abstractThis 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.
dc.description.urihttps://www.tandfonline.com/action/showCopyRight?scroll=top&doi=10.1080%2F02673843.2024.2417903
dc.description.urihttps://www.tandfonline.com/doi/full/10.1080/02673843.2024.2417903#abstract
dc.identifier.citationInternational Journal of Adolescence and Youth. Volume 29, Issue 1. 2024. Article number 2417903
dc.identifier.doi10.1080/02673843.2024.2417903
dc.identifier.issn0267-3843
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/63270
dc.language.isoen
dc.publisherRoutledge
dc.rights.licenseAttribution-NonCommercial 4.0 International Deed (CC BY-NC 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectBig Data
dc.subjectExercise
dc.subjectHealth
dc.subjectMachine Learning
dc.titleUse of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents
dc.typeArtículo
Archivos
Bloque original
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents.pdf
Tamaño:
2.16 MB
Formato:
Adobe Portable Document Format
Descripción:
TEXTO COMPLETO EN INGLÉS
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar
Colecciones
FEd - Artículos de Revista