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Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity
Allen, Ben Lane, Morgan Steeves, Elizabeth Anderson Raynor, Hollie
Allen, Ben
Lane, Morgan
Steeves, Elizabeth Anderson
Raynor, Hollie
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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.
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2022-08-02
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MDPI
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Allen_2022.pdf
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Adolescent obesity, Neighborhood education, Neighborhood poverty, Household income, Parent education, Explainable artificial intelligence, Machine learning, Ecological theory
Citation
Allen, B.; Lane, M.; Steeves, E.A.; Raynor, H. Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity. Int. J. Environ. Res. Public Health 2022, 19, 9447. https://doi.org/10.3390/ijerph19159447