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

Author

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  • Cheong Kim

    (SKK Business School, Sungkyunkwan University, Seoul 03063, Korea
    Airport Business Analytics, Economics Department, Airport Council International (ACI) World, 800 rue du Square Victoria, Suite 1810, Montreal, QC H4Z 1G8, Canada)

  • Francis Joseph Costello

    (SKK Business School, Sungkyunkwan University, Seoul 03063, Korea)

  • Kun Chang Lee

    (SKK Business School, Sungkyunkwan University, Seoul 03063, Korea
    Department of Health Sciences & Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Korea
    Creativity Science Research Institute (CSRI), Sungkyunkwan University, Seoul 03063, Korea)

  • Yuan Li

    (SKK Business School, Sungkyunkwan University, Seoul 03063, Korea)

  • Chenyao Li

    (SKK Business School, Sungkyunkwan University, Seoul 03063, Korea)

Abstract

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.

Suggested Citation

  • Cheong Kim & Francis Joseph Costello & Kun Chang Lee & Yuan Li & Chenyao Li, 2019. "Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis," IJERPH, MDPI, vol. 16(23), pages 1-18, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:23:p:4684-:d:290489
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    References listed on IDEAS

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    Cited by:

    1. Gwanggil Jeon & Abdellah Chehri, 2021. "Computing Techniques for Environmental Research and Public Health," IJERPH, MDPI, vol. 18(18), pages 1-4, September.

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