Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis
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Yáñez Sepúlveda, Rodrigo
Vásquez Bonilla, Aldo
Olivares, Rodrigo
Olivares, Pablo
Zavala Crichton, Juan Pablo
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The 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.
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Yáñez-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/s41598-025-15264-6
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