Leveraging data mining techniques to understand drivers of obesity

R Salehnejad, R Allmendinger, Y. W Chen, M Ali, Azar Shahgholian, P Yiapanis, M Mansur

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Abstract

Abstract—Substantial research has been carried out to explain the effects of economic variables on obesity, typically considering only a few factors at a time, using parametric linear regression models. Recent studies have made a significant contribution by examining economic factors affecting body weight using the Behavioral Risk Factor Surveillance System data with 27 state-level variables for a period of 20 years (1990- 2010). As elsewhere, the authors solely focus on individual effects of potential drivers of obesity than critical interactions among the drivers. We take some steps to extend the literature and gain a deeper understanding of the drivers of obesity. We employ state-of-the-art data mining techniques to uncover critical interactions that may exist among drivers of obesity in a data-driven manner. The state-of-the-art techniques reveal several complex interactions among economic and behavioral factors that contribute to the rise of obesity. Lower levels of obesity, measured by a body mass index (BMI), belong to female individuals who exercise outside work, enjoy higher levels of education and drink less alcohol. The highest level of obesity, in contrast, belongs to those who fail to exercise outside work, smoke regularly, consume more alcohol and come from lower income groups. These and other complementary results suggest that it is the joint complex interactions among various behavioral and economic factors that gives rise to obesity or lowers it; it is not simply the presence or absence of individual factors
Original languageEnglish
DOIs
Publication statusPublished - 2017
Event2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology - University of Manchester, Manchester, United Kingdom
Duration: 23 Aug 201725 Aug 2017

Conference

Conference2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
Abbreviated titleCIBCB 2017
CountryUnited Kingdom
CityManchester
Period23/08/1725/08/17

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    Salehnejad, R., Allmendinger, R., Chen, Y. W., Ali, M., Shahgholian, A., Yiapanis, P., & Mansur, M. (2017). Leveraging data mining techniques to understand drivers of obesity. Paper presented at 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, Manchester, United Kingdom. https://doi.org/10.1109/CIBCB.2017.8058521