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Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity

Author

Listed:
  • Ben Allen

    (Department of Psychology, University of Kansas, 1415 Jayhawk Blvd, Lawrence, KS 66045, USA)

  • Morgan Lane

    (Department of Psychology, University of Tennessee, Austin Peay Building, Knoxville, TN 37996, USA)

  • Elizabeth Anderson Steeves

    (Department of Nutrition, University of Tennessee, 1215 W. Cumberland Ave., Knoxville, TN 37996, USA)

  • Hollie Raynor

    (Department of Nutrition, University of Tennessee, 1215 W. Cumberland Ave., Knoxville, TN 37996, USA)

Abstract

Ecological theories suggest that environmental, social, and individual factors interact to cause obesity. Yet, many analytic techniques, such as multilevel modeling, require manual specification of interacting factors, making them inept in their ability to search for interactions. This paper shows evidence that an explainable artificial intelligence approach, commonly employed in genomics research, can address this problem. The method entails using random intersection trees to decode interactions learned by random forest models. Here, this approach is used to extract interactions between features of a multi-level environment from random forest models of waist-to-height ratios using 11,112 participants from the Adolescent Brain Cognitive Development study. This study shows that methods used to discover interactions between genes can also discover interacting features of the environment that impact obesity. This new approach to modeling ecosystems may help shine a spotlight on combinations of environmental features that are important to obesity, as well as other health outcomes.

Suggested Citation

  • Ben Allen & Morgan Lane & Elizabeth Anderson Steeves & Hollie Raynor, 2022. "Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity," IJERPH, MDPI, vol. 19(15), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9447-:d:878035
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Kristen Cooksey-Stowers & Marlene B. Schwartz & Kelly D. Brownell, 2017. "Food Swamps Predict Obesity Rates Better Than Food Deserts in the United States," IJERPH, MDPI, vol. 14(11), pages 1-20, November.
    3. Fiese, Barbara H. & Hammons, Amber & Grigsby-Toussaint, Diana, 2012. "Family mealtimes: A contextual approach to understanding childhood obesity," Economics & Human Biology, Elsevier, vol. 10(4), pages 365-374.
    4. Ken Smith & Norman Waitzman, 1994. "Double jeopardy: Interaction effects of marital and poverty status on the risk of mortality," Demography, Springer;Population Association of America (PAA), vol. 31(3), pages 487-507, August.
    5. Arun Rai, 2020. "Explainable AI: from black box to glass box," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January.
    6. Glass, Thomas A. & McAtee, Matthew J., 2006. "Behavioral science at the crossroads in public health: Extending horizons, envisioning the future," Social Science & Medicine, Elsevier, vol. 62(7), pages 1650-1671, April.
    7. Kevin M. Fitzpatrick & Don Willis, 2020. "Chronic Disease, the Built Environment, and Unequal Health Risks in the 500 Largest U.S. Cities," IJERPH, MDPI, vol. 17(8), pages 1-8, April.
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