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Aplicación de Técnicas de Machine Learning para la Predicción de la Obesidad en Jóvenes de Estados Unidos

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2022-09
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La obesidad es una enfermedad cada vez más común entre los jóvenes, que llega a niveles pandémicos en la actualidad. El hecho de padecer esta enfermedad a edades tempranas supone un grave riesgo para la salud en el corto y medio plazo y se relaciona con otro tipo de patologías como son los problemas cardiovasculares, la hipertensión o la diabetes, entre otros. Es especialmente preocupante la situación de esta enfermedad en Estados Unidos, de modo que hemos focalizado el estudio en la población joven de dicho país. En este sentido, hemos realizado un análisis de las causas que motivan esta enfermedad y hemos desarrollado un modelo predictivo de la obesidad utilizando técnicas de machine learning.
Obesity is an increasingly common disease among young people, reaching pandemic levels nowadays. Suffering from this disease at an early age represents a serious health risk in the short and medium term and is related to other types of pathologies such as cardiovascular problems, blood pressure or diabetes, among others. The situation of this disease in the United States is particularly concerning, in such way that we have focused our study on the young population of that country. In this regard, we have made an analysis of the causes that lead to this disease and developed a predictive model of obesity using machine learning techniques.
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Borràs, P. A., & Ugarriza, L. (2013). Obesidad infantil: ¿nos estamos equivocando? Principales causas del problema y tendencias de investigación. Apunts Medicina de l Esport, 48(178), 63–68. https://doi.org/10.1016/j.apunts.2012.09.004 Bryan, S., Afful, J., Carroll, M., Te-Ching, C., Orlando, D., Fink, S., & Fryar, C. (2021). NHSR 158. National health and nutrition examination survey 2017-March 2020 Pre-pandemic Data Files. Calviño, A. (2020). Material de la asignatura Técnicas y Metodología de la Minería de Datos (SEMMA). Colmenarejo, G. (2020). Machine Learning models to predict childhood and adolescent obesity: A review. Nutrients, 12(8), 2466. Cutler, A., Cutler, D. R., & Stevens, J. R. (2012). Random Forests. Ensemble Machine Learning. 157–175. https://doi.org/10.1007/978-1-4419-9326-7_5 Dugan, T. M., Mukhopadhyay, S., Carroll, A., & Downs, S. (2015). Machine learning techniques for prediction of early childhood obesity. Applied Clinical Informatics, 6(3), 506–520. https://doi.org/10.4338/ACI-2015-03-RA-0036 Howard, J. (2018). Childhood obesity: America's 'true national crisis' measured state by state. CNN. Manios, Y., Vlachopapadopoulou, E., Moschonis, G., Karachaliou, F., Psaltopoulou, T., Koutsouki, D., Bogdanis, G., Carayanni, V., Hatzakis, A., & Michalacos, S. (2016). Utility and applicability of the “Childhood Obesity Risk Evaluation” (CORE)-index 54 in predicting obesity in childhood and adolescence in Greece from early life: the “National Action Plan for Public Health”. European Journal of Pediatrics, 175(12), 1989–1996. https://doi.org/10.1007/s00431-016-2799-2 Morandi, A., Meyre, D., Lobbens, S., Kleinman, K., Kaakinen, M., Rifas-Shiman, S. L., Vatin, V., Gaget, S., Pouta, A., Hartikainen, A.-L., Laitinen, J., Ruokonen, A., Das, S., Khan, A. A., Elliott, P., Maffeis, C., Gillman, M. W., Järvelin, M.-R., & Froguel, P. (2012). Estimation of newborn risk for child or adolescent obesity: Lessons from longitudinal birth cohorts. PloS One, 7(11), e49919. https://doi.org/10.1371/journal.pone.0049919 Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567 https://doi.org/10.1038/nbt1206-1565 Pochini, A., Wu, Y., & Hu, G. (2014). Data mining for lifestyle risk factors associated with overweight and obesity among adolescents. 2014 IIAI 3rd International Conference on Advanced Applied Informatics. Portela, J. (2021). Material de la asignatura Técnicas de Machine Learning. Products - health E stats - prevalence of Overweight and Obesity Among Children and Adolescents Aged 2–19 Years: United States, 1963–1965 through 2013–2014. (2020, mayo 8). Cdc.gov. https://www.cdc.gov/nchs/data/hestat/obesity_child_15_16/obesity_child_15_16.htm Redsell, S. A., Weng, S., Swift, J. A., Nathan, D., & Glazebrook, C. (2016). Validation, optimal threshold determination, and clinical utility of the infant risk of overweight checklist for early prevention of child overweight. Childhood Obesity, 12(3), 202–209. https://doi.org/10.1089/chi.2015.0246 Rehkopf, D. H., Laraia, B. A., Segal, M., Braithwaite, D., & Epel, E. (2011). The relative importance of predictors of body mass index change, overweight and obesity in adolescent girls. International Journal of Pediatric Obesity: IJPO: An Official Journal of the International Association for the Study of Obesity, 6(2–2), e233-42. https://doi.org/10.3109/17477166.2010.545410 SAS help center. (s/f). Sas.com. Recuperado el 10 de julio de 2022, de https://documentation.sas.com/doc/en/emref/14.3/n061bzurmej4j3n1jnj8bbjjm1a2.htm Steur, M., Smit, H. A., Schipper, C. M. A., Scholtens, S., Kerkhof, M., de Jongste, J. C., Haveman-Nies, A., Brunekreef, B., & Wijga, A. H. (2011). Predicting the risk of newborn children to become overweight later in childhood: the PIAMA birth cohort study. International Journal of Pediatric Obesity: IJPO: An Official Journal of the International Association for the Study of Obesity, 6(2–2), e170-8. https://doi.org/10.3109/17477166.2010.519389 Touzani, S., Granderson, J., & Fernandes, S. (2018). Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy and Buildings, 158, 1533–1543. https://doi.org/10.1016/j.enbuild.2017.11.039 Una guía para la prueba de esfericidad de Bartlett. (2021, mayo 15). Statologos: El sitio web para que aprendas estadística en Stata, R y Phyton. https://statologos.com/prueba-de-bartletts-de-esfericidad/ Weng, S. F., Redsell, S. A., Nathan, D., Swift, J. A., Yang, M., & Glazebrook, C. (2013). Estimating overweight risk in childhood from predictors during infancy. Pediatrics, 132(2), e414-21. https://doi.org/10.1542/peds.2012-3858 YRBSS Data & documentation. (2022, mayo 2). Cdc.gov. https://www.cdc.gov/healthyyouth/data/yrbs/data.htm Zheng, Z., & Ruggiero, K. (2017). Using machine learning to predict obesity in high school students. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).