Detection of variables for the diagnosis of overweight and obesity in young Chileans using machine learning techniques

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dc.contributor.authorCalderon-Diaz, Mailyn
dc.contributor.authorSerey-Castillo, Leonardo J.
dc.contributor.authorVallejos-Cuevas, Esperanza A.
dc.contributor.authorEspinoza, Alexis
dc.contributor.authorSalas, Rodrigo
dc.contributor.authorMacias-Jimenez, Mayra A.
dc.date.accessioned2024-04-16T20:24:39Z
dc.date.available2024-04-16T20:24:39Z
dc.date.issued2023-03
dc.descriptionIndexación: Scopus.
dc.description.abstractOverweight and obesity are considered epidemic problems. The number of factors involved in developing extra body fat makes harder the detection of this problem. Therefore, among the several variables and their levels presented in overweight and obese people, there is a need to improve the classification of people with these conditions. To this aim, in this paper, we conducted a variable analysis from biochemical and lipid profiles in young Chileans with normal weight, overweight, and obesity using machine learning techniques. XGBoost library was selected as the classifier. 21 variables (13 from biochemical and 8 from lipid profiles) were chosen as features. 100 iterations were conducted, and an 80% cross-validation was obtained. The variables with greater relevance in the classification task were total cholesterol, glycemia, LDH enzyme, bilirubin, and VLDL cholesterol. All of these, except bilirubin, are consistent with previous research in which these features have been used to assess risk factors for developing overweight or obesity. Then, further research must include a deep study regarding bilirubin's influence over these conditions. © 2023 Elsevier B.V.. All rights reserved.
dc.description.urihttps://www-sciencedirect-com.recursosbiblioteca.unab.cl/science/article/pii/S1877050923006701?via%3Dihub
dc.identifier.citationProcedia Computer Science. Volume 220, Pages 978 - 983. 2023 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023. Leuven. 15 March 2023through 17 March 2023. Code 189712
dc.identifier.doi10.1016/j.procs.2023.03.135
dc.identifier.issn1877-0509
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/56054
dc.language.isoen
dc.publisherElsevier B.V.
dc.rights.licenseCC BY-NC-ND 4.0 DEED Attribution-NonCommercial-NoDerivs 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBiochemical profiles
dc.subjectClassification
dc.subjectLipid profiles
dc.subjectMachine learning
dc.subjectNormal-weight
dc.subjectObesity
dc.subjectOverweight
dc.titleDetection of variables for the diagnosis of overweight and obesity in young Chileans using machine learning techniques
dc.typeArtículo
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