Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis
Year: 2025
Type: article
Study design: Observational Study
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). Six supervised machine learning models, random forest, gradient koosting, k-nearest neighbors, logistic regression, support vector machine, and decision tree, were trained and evaluated using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), and SHapley Additive exPlanations value explanations. Random forest outperformed all other models, achieving the highest accuracy (84.2%), F1-score (83.7%), and AUC-ROC (0.947). SHapley Additive exPlanations analysis revealed that FMI, FFMI, and BMI were the most influential features, while sex had minimal predictive impact. Machine learning models, particularly tree-based algorithms like random forest, show great potential in classifying obesity levels from anthropometric data with high accuracy and interpretability. These models can enhance the effectiveness of obesity screening in clinical and community settings.
Source: Scientific Reports
Authors: Rodrigo Yáñez-Sepúlveda, Aldo Vásquez-Bonilla, Rodrigo Olivares, Pablo Olivares, Juan Pablo Zavala-Crichton, Claudio Hinojosa-Torres, Catalina Muñoz-Strale, Frano Giakoni-Ramírez, Josivaldo de Souza-Lima, Jacqueline Páez-Herrera, Jorge Olivares-Arancibia, Tomás Reyes-Amigo, Guillermo Cortés-Roco, Juan Hurtado-Almonacid, Eduardo Guzmán-Muñoz, Nicole Aguilera-Martínez, José Francisco López-Gil, Boryi A. Becerra-Patiño, Juan David Paucar-Uribe, Exal Garcia-Carrillo, Vicente Javier Clemente-Suárez
Institutions: Universidad Andrés Bello, Universidad de Extremadura, University of Valparaíso, Pontificia Universidad Católica de Valparaíso, University of the Americas, Viña del Mar University, Universidad Autónoma de Chile, Universidad Santo Tomás, Catholic University of the Maule, Universidad Espíritu Santo, Universidad de Los Lagos, Universidad Pedagógica Nacional, Universidad Bernardo O'Higgins, University of the Coast, Universidad Europea de Madrid
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W4413366969