Yáñez Sepúlveda, RodrigoOlivares, RodrigoOlivares, PabloZavala Crichton, Juan PabloHinojosa Torres, ClaudioGiakoni Ramírez, FranoSouza Lima, Josivaldo deMonsalves Álvarez, MatíasTuesta, MarceloClemente Suárez, Vicente JavierEt. al.2026-07-252026-07-252025Yáñez-Sepúlveda, R., Olivares, R., Olivares, P., Zavala-Crichton, J. P., Hinojosa-Torres, C., Giakoni-Ramírez, F., Souza-Lima, J. D., Monsalves-Álvarez, M., Tuesta, M., Páez-Herrera, J., Olivares-Arancibia, J., Reyes-Amigo, T., Cortés-Roco, G., Hurtado-Almonacid, J., Guzmán-Muñoz, E., Aguilera-Martínez, N., López-Gil, J. F., & Clemente-Suárez, V. J. (2025). Supervised machine learning algorithms for fitness-based cardiometabolic risk classification in adolescents. Sports, 13(8), 273. https://doi.org/10.3390/sports130802732075-4663https://hdl.handle.net/11268/17323Cardiometabolic risk in adolescents represents a growing public health concern that is closely linked to modifiable factors such as physical fitness. Traditional statistical approaches often fail to capture complex, nonlinear relationships among anthropometric and fitness-related variables. This study aims to develop and evaluate supervised machine learning algorithms, including artificial neural networks and ensemble methods, for classifying cardiometabolic risk levels among Chilean adolescents based on standardized physical fitness assessments.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Impulso de los algoritmos de aprendizaje automáticoAptitud físicaAdolescentejournal article10.3390/sports13080273open accessDeporteInvestigación médicaMedicina preventivaGoal 3: Ensure healthy lives and promote well-being for all at all ages