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

Gradient boosting proved to be the most effective model for predicting cardiometabolic risk based on physical fitness data. This approach offers a practical, data-driven tool for early risk detection in adolescent populations and may support scalable screening efforts in educational and clinical settings.

Authors

Institutions

Publication Details

Journal
Sports
Published
2025-08-18
DOI
https://doi.org/10.3390/sports13080273
Primary Topic
Cardiovascular and exercise physiology
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Claudio Hinojosa-Torres, Eduardo Guzmán-Muñoz, Rodrigo Olivares, Jorge Olivares‐Arancibia et al.
Sports
Cardiovascular and exercise physiology
article

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

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

Abstract

Gradient boosting proved to be the most effective model for predicting cardiometabolic risk based on physical fitness data. This approach offers a practical, data-driven tool for early risk detection in adolescent populations and may support scalable screening efforts in educational and clinical settings.

SportsVol. 13(8)
Universidad Andrés Bello (CL), Catholic University of the Maule (CL), Universidad Autónoma de Chile (CL), Universidad Espíritu Santo (EC), Viña del Mar University (CL), Universidad de Playa Ancha de Ciencias de la Educación (CL), Universidad Andrés Bello (CL), University of the Coast (CO), Pontificial Catholic University of Valparaiso (CL), Universidad Santo Tomás (CL), Universidad de Los Lagos (CL), Departamento de Educación (ES), University of Valparaíso (CL), Universidad Europea de Madrid (ES)
Ministerio de Educación, Gobierno de Chile
Openalex Percentile: Top 67%
Cardiovascular and exercise physiology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.