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dc.creatorCañas Cervantes, Rodolfo
dc.creatorMartinez Palacio, Ubaldo
dc.date.accessioned2021-01-07T18:35:58Z
dc.date.available2021-01-07T18:35:58Z
dc.date.issued2020
dc.identifier.issn2352-9148
dc.identifier.urihttps://hdl.handle.net/11323/7668
dc.description.abstractObesity is a worldwide disease that affects people of all ages and gender; in consequence, researchers have made great efforts to identify factors that cause it early. In this study, an intelligent method is created, based on supervised and unsupervised techniques of data mining such as Simple K-Means, Decision Trees (DT), and Support Vector Machines (SVM) to detect obesity levels and help people and health professionals to have a healthier lifestyle against this global epidemic. In this research the primary source of collection was from students 18 and 25 years old at institutions in the countries of Colombia, Mexico, and Peru. The study takes a dataset relating to the main causes of obesity, based on the aim to reference high caloric intake, a decrease of energy expenditure due to the lack of physical activity, alimentary disorders, genetics, socioeconomic factors, and/or anxiety and depression. In the selected dataset, 178 students participated in the study, 81 male and 97 female. Using algorithms including Decision Tree, Support Vector Machine (SVM), and Simple K-Means, the results show a relevant tool to perform a comparative analysis among the mentioned algorithms.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceInformatics in Medicine Unlockedspa
dc.subjectObesityspa
dc.subjectSimple k-meansspa
dc.subjectDecision treesspa
dc.subjectSupport vector machinesspa
dc.titleEstimation of obesity levels based on computational intelligencespa
dc.typearticlespa
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dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S2352914820306225spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.imu.2020.100472


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