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

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 e...

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Bibliographic Details
Published in:International journal of environmental research and public health 2022-08, Vol.19 (15), p.9447
Main Authors: Allen, Ben, Lane, Morgan, Steeves, Elizabeth Anderson, Raynor, Hollie
Format: Article
Language:English
Subjects:
Age
Algorithms
Artificial intelligence
Behavior
Body mass index
Cognitive ability
Ecological models
Ecosystems
Ethnicity
Feature selection
Hispanic Americans
Households
Obesity
Puberty
Standard deviation
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Summary: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.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph19159447