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Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis

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2025-08

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journal article

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Yáñez-Sepúlveda, Rodrigo, Vásquez-Bonilla, Aldo, Olivares, Rodrigo, Olivares, Pablo, Zavala-Crichton, Juan Pablo, Hinojosa-Torres, Claudio, Muñoz-Strale, Catalina, Giakoni-Ramírez, Frano, de Souza-Lima, Josivaldo, 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, Becerra-Patiño, Boryi A., Paucar-Uribe, Juan David, Garcia-Carrillo, Exal, Clemente-Suárez, Vicente Javier

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Nature Research

URI

https://repositorio.udla.cl/handle/udla/2209

DOI

https://doi.org/10.1038/s41598-025-15264-6

Keywords

Bioelectrical impedance analysis, Body composition, Machine learning, SHAP values, Supervised algorithms

Citation

Yáñez-Sepúlveda, Rodrigo; Vásquez-Bonilla, Aldo; Olivares, Rodrigo; Olivares, Pablo; Zavala-Crichton, Juan Pablo; Hinojosa-Torres, Claudio; Muñoz-Strale, Catalina; Giakoni-Ramírez, Frano; de Souza-Lima, Josivaldo; 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; Becerra-Patiño, Boryi A.; Paucar-Uribe, Juan David; Garcia-Carrillo, Exal; Clemente-Suárez, Vicente Javier (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

Abstract

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 differentiatin...

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