Publication:
Supervised Machine Learning Algorithms for Fitness-Based Cardiometabolic Risk Classification in Adolescents

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Date

2025-09

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journal article

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Authors

Yáñez-Sepúlveda, Rodrigo, Olivares, Rodrigo, Olivares, Pablo, Zavala-Crichton, Juan Pablo, Hinojosa-Torres, Claudio, Giakoni-Ramírez, Frano, Souza-Lima, Josivaldo de, Monsalves-Álvarez, Matías, Tuesta, Marcelo, Páez-Herrera, Jacqueline, Olivares-Arancibia, Jorge, Reyes-Amigo, Tomás, Cortés-Roco, Guillermo, Hurtado-Almonacid, Juan, Guzmán-Muñoz, Eduardo, Aguilera-Martínez, Nicole, López-Gil, José Francisco, Clemente-Suárez, Vicente Javier

Publisher

Multidisciplinary Digital Publishing Institute (MDPI)

URI

https://repositorio.udla.cl/handle/udla/2214

DOI

https://doi.org/10.3390/sports13080273

Keywords

gradient boosting, health, physical fitness, adolescent, predictive modeling

Citation

Yáñez-Sepúlveda, Rodrigo; Olivares, Rodrigo; Olivares, Pablo; Zavala-Crichton, Juan Pablo; Hinojosa-Torres, Claudio; Giakoni-Ramírez, Frano; Souza-Lima, Josivaldo de; Monsalves-Álvarez, Matías; Tuesta, Marcelo; Páez-Herrera, Jacqueline; Olivares-Arancibia, Jorge; Reyes-Amigo, Tomás; Cortés-Roco, Guillermo; Hurtado-Almonacid, Juan; Guzmán-Muñoz, Eduardo; Aguilera-Martínez, Nicole; López-Gil, José Francisco; Clemente-Suárez, Vicente Javier (2025). Supervised Machine Learning Algorithms for Fitness-Based Cardiometabolic Risk Classification in Adolescents. Sports, 13(8), 273. https://doi.org/10.3390/sports13080273

Abstract

Background: Cardiometabolic risk in adolescents represents a growing public health concern that is closely linked to modifiable factors such as physical fitness. Traditional statistical approaches oft...

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