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

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

SUBMITTER: Allen B 

PROVIDER: S-EPMC9367834 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature