{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15(1)"],"submitter":["Yanez-Sepulveda R"],"pubmed_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 differentiating between fat and lean mass. This study aimed to evaluate and compare the performance of various supervised machine learning algorithms in classifying obesity levels using anthropometric indices derived from bioelectrical impedance analysis (BIA). A cross-sectional study was conducted on a sample of 5372 adults (age 34.6 ± 10.0 years) (2727 females and 2645 males). Anthropometric data included BMI, fat mass index (FMI), fat-free mass index (FFMI), skeletal muscle index (SMI), muscle mass index (MM), and others were collected using a validated multifrequency octopolar BIA device (InBody 270). S"],"journal":["Scientific reports"],"pagination":["30681"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12371046"],"repository":["biostudies-literature"],"pubmed_title":["Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis."],"pmcid":["PMC12371046"],"pubmed_authors":["Paez-Herrera J","Reyes-Amigo T","Olivares R","Hinojosa-Torres C","Olivares P","Cortes-Roco G","Aguilera-Martinez N","Becerra-Patino BA","Munoz-Strale C","Lopez-Gil JF","Clemente-Suarez VJ","Guzman-Munoz E","Garcia-Carrillo E","Paucar-Uribe JD","Hurtado-Almonacid J","Zavala-Crichton JP","Giakoni-Ramirez F","Yanez-Sepulveda R","Olivares-Arancibia J","de Souza-Lima J","Vasquez-Bonilla A"],"additional_accession":[]},"is_claimable":false,"name":"Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis.","description":"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 aimed to evaluate and compare the performance of various supervised machine learning algorithms in classifying obesity levels using anthropometric indices derived from bioelectrical impedance analysis (BIA). A cross-sectional study was conducted on a sample of 5372 adults (age 34.6 ± 10.0 years) (2727 females and 2645 males). Anthropometric data included BMI, fat mass index (FMI), fat-free mass index (FFMI), skeletal muscle index (SMI), muscle mass index (MM), and others were collected using a validated multifrequency octopolar BIA device (InBody 270). S","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-05-09T10:33:46.523Z","creation":"2026-04-08T00:46:49.434Z"},"accession":"S-EPMC12371046","cross_references":{"pubmed":["40841553"],"doi":["10.1038/s41598-025-15264-6"]}}