Yáñez Sepúlveda, RodrigoVásquez Bonilla, AldoOlivares, RodrigoOlivares, PabloZavala Crichton, Juan PabloHinojosa Torres, ClaudioMuñoz Strale, CatalinaGiakoni Ramírez, FranoSouza Lima, Josivaldo deClemente Suárez, Vicente JavierEt al.2025-09-272025-09-272025Yáñez-Sepúlveda, R., Vásquez-Bonilla, A., Olivares, R., Olivares, P., Zavala-Crichton, J. P., Hinojosa-Torres, C., Muñoz-Strale, C., Giakoni-Ramírez, F., De Souza-Lima, J., 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., Becerra-Patiño, B. A., Paucar-Uribe, J. D., … Clemente-Suárez, V. J. (2025). Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis. Scientific Reports, 15(1), 30681. https://doi.org/10.1038/s41598-025-15264-62045-2322https://hdl.handle.net/11268/16240The accurate classification of obesity is essential for public health and clinical decision-making. Traditional anthropometric measures such as body mass index (BMI) have limitations in differentiating between fat and lean mass. This study aimes to evaluate and compare the performance of various supervised machine learning algorithms in classifying obesity levels using anthropometric indices derived from bioelectrical impedance analysis.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/journal article10.1038/s41598-025-15264-6open accessObesidadAprendizajeBiofísicaGoal 3: Ensure healthy lives and promote well-being for all at all agesGoal 5: Achieve gender equality and empower all women and girlsGoal 16: Promote just, peaceful and inclusive societies