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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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