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Article

Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis

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SKK Business School, Sungkyunkwan University, Seoul 03063, Korea
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Airport Business Analytics, Economics Department, Airport Council International (ACI) World, 800 rue du Square Victoria, Suite 1810, Montreal, QC H4Z 1G8, Canada
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Department of Health Sciences & Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Korea
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Creativity Science Research Institute (CSRI), Sungkyunkwan University, Seoul 03063, Korea
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(23), 4684; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16234684
Received: 4 October 2019 / Revised: 21 November 2019 / Accepted: 22 November 2019 / Published: 25 November 2019
(This article belongs to the Special Issue Computing Techniques for Environmental Research and Public Health)
With the remarkable improvement in people’s socioeconomic living standards around the world, adolescent obesity has increasingly become an important public health issue that cannot be ignored. Thus, we have implemented its use in an attempt to explore the viability of scenario-based simulations through the use of a data mining approach. In doing so, we wanted to explore the merits of using a General Bayesian Network (GBN) with What-If analysis while exploring how it can be utilized in other areas of public health. We analyzed data from the 2017 Korean Youth Health Behavior Survey conducted directly by the Korea Centers for Disease Control & Prevention, including 19 attributes and 11,206 individual data points. Our simulations found that by manipulating the amount of pocket money-between $60 and $80-coupled with a low-income background, it has a high potential to increase obesity compared with other simulated factors. Additionally, when we manipulated an increase in studying time with a mediocre academic performance, it was found to potentially increase pressure on adolescents, which subsequently led to an increased obesity outcome. Lastly, we found that when we manipulated an increase in a father’s education level while manipulating a decrease in mother’s education level, this had a large effect on the potential adolescent obesity level. Although obesity was the chosen case, this paper acts more as a proof of concept in analyzing public health through GBN and What-If analysis. Therefore, it aims to guide health professionals into potentially expanding their ability to simulate certain outcomes based on predicted changes in certain factors concerning future public health issues. View Full-Text
Keywords: public health; health informatics; adolescent obesity; General Bayesian Network; what-if analysis; data mining public health; health informatics; adolescent obesity; General Bayesian Network; what-if analysis; data mining
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MDPI and ACS Style

Kim, C.; Costello, F.J.; Lee, K.C.; Li, Y.; Li, C. Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis. Int. J. Environ. Res. Public Health 2019, 16, 4684. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16234684

AMA Style

Kim C, Costello FJ, Lee KC, Li Y, Li C. Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis. International Journal of Environmental Research and Public Health. 2019; 16(23):4684. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16234684

Chicago/Turabian Style

Kim, Cheong, Francis J. Costello, Kun C. Lee, Yuan Li, and Chenyao Li. 2019. "Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis" International Journal of Environmental Research and Public Health 16, no. 23: 4684. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16234684

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