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

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dc.contributor.authorYáñez Sepúlveda, Rodrigo
dc.contributor.authorVásquez Bonilla, Aldo
dc.contributor.authorOlivares, Rodrigo
dc.contributor.authorOlivares, Pablo
dc.contributor.authorZavala Crichton, Juan Pablo
dc.contributor.authorHinojosa Torres, Claudio
dc.contributor.authorMuñoz Strale, Catalina
dc.contributor.authorGiakoni Ramírez, Frano
dc.contributor.authorSouza Lima, Josivaldo de
dc.contributor.authorClemente Suárez, Vicente Javier
dc.contributor.authorEt al.
dc.date.accessioned2025-09-27T12:33:56Z
dc.date.available2025-09-27T12:33:56Z
dc.date.issued2025
dc.description.abstractThe 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. en
dc.description.filiationUEMspa
dc.description.impact3.9 Q1 JCR 2024
dc.description.impact0.874 Q1 SJR 2024
dc.description.impactNo data IDR 2023
dc.description.sponsorshipSin financiaciónes
dc.identifier.citationYáñez-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
dc.identifier.doi10.1038/s41598-025-15264-6
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/11268/16240
dc.language.isoeng
dc.peerreviewedSi
dc.relation.publisherversionhttps://doi.org/10.1038/s41598-025-15264-6
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.sdgGoal 3: Ensure healthy lives and promote well-being for all at all ages
dc.subject.sdgGoal 5: Achieve gender equality and empower all women and girls
dc.subject.sdgGoal 16: Promote just, peaceful and inclusive societies
dc.subject.unescoObesidad
dc.subject.unescoAprendizaje
dc.subject.unescoBiofísica
dc.titleSupervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis
dc.typejournal article
dc.type.hasVersionVoR
dspace.entity.typePublication
relation.isAuthorOfPublicationa2e25626-16b1-41bc-9c67-8de8ce6e007d
relation.isAuthorOfPublication.latestForDiscoverya2e25626-16b1-41bc-9c67-8de8ce6e007d

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