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dc.creatorMendoza Palechor, Fabio
dc.creatorde la Hoz Manotas, Alexis
dc.date.accessioned2019-09-04T22:44:23Z
dc.date.available2019-09-04T22:44:23Z
dc.date.issued2019-08
dc.identifier.issn2352-3409
dc.identifier.urihttp://hdl.handle.net/11323/5236
dc.description.abstractThis paper presents data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2111 records, the records are labeled with the class variable NObesity (Obesity Level), that allows classification of the data using the values of Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II and Obesity Type III. 77% of the data was generated synthetically using the Weka tool and the SMOTE filter, 23% of the data was collected directly from users through a web platform. This data can be used to generate intelligent computational tools to identify the obesity level of an individual and to build recommender systems that monitor obesity levels. For discussion and more information of the dataset creation, please refer to the full-length article “Obesity Level Estimation Software based on Decision Trees” (De-La-Hoz-Correa et al., 2019).spa
dc.description.sponsorshipUniversidad de la Costaspa
dc.language.isoengspa
dc.publisherData in Briefspa
dc.relation.ispartofhttps://doi.org/10.1016/j.dib.2019.104344spa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectObesityspa
dc.subjectData miningspa
dc.subjectWekaspa
dc.subjectSMOTEspa
dc.titleDataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexicospa
dc.typeArticlespa
dcterms.references[1] M.V. Olmedo, La obesidad: un problema de salud pública. Revista de divulgaci o científica y tecnol ogica de la Universidad Veracruzana, 2011. Recuperado de: https://www.uv.mx/cienciahombre/revistae/vol24num3/articulos/obesidad/. [2] C. Davila-Payan, M. DeGuzman, K. Johnson, N. Serban, J. Swann, Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data, Prev. Chronic Dis. 12 (2015). [3] S. Manna, A.M. Jewkes, Understanding early childhood obesity risks: an empirical study using fuzzy signatures, in: Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on, IEEE, 2014, July, pp. 1333e1339. [4] M.H.B.M. Adnan, W. Husain, A hybrid approach using Naïve Bayes and Genetic Algorithm for childhood obesity prediction, in: Computer & Information Science (ICCIS), 2012 International Conference on vol. 1, IEEE, 2012, June, pp. 281e285. [5] T.M. Dugan, S. Mukhopadhyay, A. Carroll, S. Downs, Machine learning techniques for prediction of early childhood obesity, Appl. Clin. Inf. 6 (3) (2015) 506e520. [6] Eduardo De-La-Hoz-Correa, Fabio E. Mendoza-Palechor, Alexis De-La-Hoz-Manotas, Roberto C. Morales-Ortega, Beatriz Adriana S anchez Hern andez, Obesity level estimation software based on decision Trees, J. Comput. Sci. 15 (Issue 1) (2019) 67e77, https://doi.org/10.3844/jcssp.2019.67.77. [7] DO, NORMA Oficial Mexicana NOM-008-SSA3-2010, Para el tratamiento integral del sobrepeso y la obesidad, Diario Oficial,2010. [8] N.V. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer, SMOTE: synthetic minority over-sampling technique, J. Artif. Intell. Res. 16 (2002) 321e357.spa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa


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