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dc.creatorCañas Cervantes, Rodolfo
dc.creatorMartinez Palacio, Ubaldo
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
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.sourceInformatics in Medicine Unlockedspa
dc.subjectSimple k-meansspa
dc.subjectDecision treesspa
dc.subjectSupport vector machinesspa
dc.titleEstimation of obesity levels based on computational intelligencespa
dcterms.references[1] Guti´errez HM. Diez problemas de la Poblacion ´ de Jalisco: una perspectiva sociodemografica. ´ In: Edicion ´ Primera, editor. Guadalajara, M´exico. Direccion ´ de Publicaciones del Gobierno de Jalisco;
dcterms.references[2] OMS. Organizacion ´ mundial de la Salud. 2016. re/factsheets/fs311/es/.spa
dcterms.references[3] Olmedo, M. V. “La obesidad: un problema de salud pública. 2016 Revista de divulgacion ´ científica y tecnologica ´ de la Universidad Veracruzana. Reference to a journal publication with an article
dcterms.references[4] Hernández GM. Prevalencia de sobrepeso y obesidad, y factores de riesgo, en ninos ˜ de 7-12 anos, ˜ en una escuela pública de Cartagena. Universidad Nacional de Colombia (Colombia), septiembre - octubre;
dcterms.references[5] Davila-Payan C, DeGuzman M, Johnson K, Serban N, Swann J. Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data. Prev Chronic Dis 2015;12:140229. https://doi. org/10.5888/
dcterms.references[6] Manna, S., & Jewkes, A. M. “Understanding early childhood obesity risks: an empirical study using fuzzy signatures”, In Fuzzy systems (FUZZ-IEEE). 2014 IEEE international conference on (pp. 1333-1339).
dcterms.references[7] Adnan MHBM, Husain W. 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. p. 281–
dcterms.references[8] Adnan MHM, Husain W. A framework for childhood obesity classifications and predictions using NBtree”. In: Information technology in asia (CITA 11), 2011 7th international conference on. IEEE; 2011. p. 1–
dcterms.references[9] Adnan, M. H. B. M., Husain, W., & Damanhoori, F. “A survey on utilization of data mining for childhood obesity prediction”, In Information and telecommunication technologies (APSITT). 2010 8th asia-pacific symposium on (pp. 1-6).
dcterms.references[10] Dugan TM, Mukhopadhyay S, Carroll A, Downs S. Machine learning techniques for prediction of early childhood obesity. Appl Clin Inf 2015;6(3):506–
dcterms.references[11] Zhang ML, Zhou ZH. Multi-instance clustering with applications to multi-instance prediction. Appl Intell 2009;31(1):47–
dcterms.references[12] Suguna M. Childhood obesity epidemic analysis using classification algorithms. Int. J. Mod. Comput. Sci 2016;4(1):22–
dcterms.references[13] Abdullah FS, Manan NSA, Ahmad A, Wafa SW, Shahril MR, Zulaily N, Ahmed A. Data mining techniques for classification of childhood obesity among year 6 school children. In: International conference on soft computing and data mining. Cham: Springer; 2016. p. 465–
dcterms.references[14] De-La-Hoz-Correa Eduardo, Mendoza-Palechor Fabio E, De-La-Hoz-Manotas Alexis, Morales-Ortega Roberto C, Sanchez ´ Hernandez ´ Beatriz Adriana. Obesity level estimation software based on decision trees. J Comput Sci 2019;15(Issue 1):67–77.
dcterms.references[15] Ward ZJ, Long MW, Resch SC, Gortmaker SL, Cradock AL, Giles C, et al. Redrawing the US obesity landscape: bias-corrected estimates of state-specific adult obesity prevalence. PloS One 2016;11(3):e0150735.
dcterms.references[16] Gomez ´ M, Avila ´ L. La obesidad: un factor de riesgo cardiometabolico. ´ In: Medicina de Familia, vol. 8; 2008. p. 91–7. Nº.
dcterms.references[17] Joachims T. Text categorization with support vector machines. Proceedings of the European C~njerence on machine learning. Springer-Verlrtg;
dcterms.references[18] Kim Y, Ling H. Human activity classification based on micro-Doppler signatures using a support vector machine”. IEEE Trans Geosci Rem Sens 2009;47(5): 1328–
dcterms.references[19] Da Silva F, Niedermeyer E. “Electroencephalography: basic principles. 1993. Clinical applications, and related fields”, William & Wikins,
dcterms.references[20] Parsons TD, Rizzo AA. Affective outcomes of virtual reality exposure therapy for anxiety and specific phobias: a meta-analysis. J Behav Ther Exp Psychiatr 2008;39 (3):250–
dcterms.references[21] De la Hoz E, de la Hoz E, Ortiz A, Ortega J, Martínez-Alvarez ´ A. Feature selection by multi-objective optimisation: application to network anomaly detection by hierarchical self-organising maps. Knowl-Based Syst 2014;71:322–
dcterms.references[22] Bekele E, Wade J, Bian D, Fan J, Swanson A, Warren Z, Sarkar N. Multimodal adaptive social interaction in virtual environment (MASI-VR) for children with Autism spectrum disorders (ASD). In: 2016 IEEE virtual reality. VR; 2016. p. 121–30.
dcterms.references[23] Han J, Kamber M. Data mining: concepts and techniques. second ed. San Francisco: Morgan Kaufmann Publishers;
dcterms.references[24] Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference and prediction. New York: Springer;
dcterms.references[25] Xu R, Wunsch D. Survey of clustering algorithms. IEEE Trans Neural Network 2005;16(3):645–
dcterms.references[26] Zhang ML, Zhou ZH. Multi-instance clustering with applications to multi-instance prediction. Appl Intell 2009;31(1):47–
dcterms.references[27] Palechor FM, De la Hoz Manotas A, Colpas PA, Ojeda JS, Ortega RM, Melo MP. Cardiovascular disease analysis using supervised and unsupervised data mining techniques. J SW 2017;12(2):81–
dcterms.references[28] Mendoza-Palechor F, Menezes ML, Sant’Anna A, Ortiz-Barrios M, Samara A, Galway L. Affective recognition from EEG signals: an integrated data-mining approach. Journal of Ambient Intelligence and Humanized Computing 2018:1–
dcterms.references[29] Fabio Mendoza-Palechor, Alexis De la Hoz-Manotas, Roberto Morales-Ortega, Ubaldo Martinez-Palacio, Jorge Diaz-Martinez, Harold Combita-Nino. Designing A method for alcohol consumption prediction based on clustering and support vector machines. Res J Appl Sci Eng Technol 2017;14(4):146–
dcterms.references[30] Palechor FM, Manotas ADLH, Franco EDLH, Colpas PA. Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems. J Theor Appl Inf Technol 2015;82(2)
dcterms.references[31] Salley James N, Hoover Adam W, Wilson Michael L, Muth Eric R. Comparison between human and bite-based methods of estimating caloric intake. J Acad Nutr Diet 2016;116(Issue 10):1568–77. ISSN 2212-2672,
dcterms.references[32] Zhu J, Pande A, Mohapatra P, Han JJ. Using deep learning for energy expenditure estimation with wearable sensors. In: 2015 17th international conference on Ehealth networking. Boston, MA: Application & Services (HealthCom); 2015. p. 501–6.
dcterms.references[33] Hall R, Pasipanodya J, Swancutt M, Meek C, Leff R, Gumbo T. Supervised machinelearning reveals that old and obese people achieve low dapsone concentrations. CPT Pharmacometrics Syst Pharmacol 2017;6:552–9.
dcterms.references[34] Gerl MJ, et al. Machine learning of human plasma lipidomes for obesity estimation in a large population cohort. PLoS Biol Oct. 2019;17(10):e3000443. https://doi. org/10.1371/journal.pbio.3000443 [Online].
dcterms.references[35] Craig A. Biwer. Computing obesity: signal processing and machine learning applied to predictive modeling of clinical weight-loss. 2017. https://deepblue.lib.umich.ed u/bitstream/handle/2027.42/140907/cbiwer_1.pdf?sequence=1&

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