Spatial machine learning for predicting physical inactivity prevalence from socioecological determinants in Chicago, Illinois, USA.

ABSTRACT: The increase in physical inactivity prevalence in the USA has been associated with neighborhood characteristics. While several studies have found an association between neighborhood and health, the relative importance of each component related to physical inactivity or how this value varies geographically (i.e., across different neighborhoods) remains unexplored. This study ranks the contribution of seven socioecological neighborhood factors to physical inactivity prevalence in Chicago, Illinois, using machine learning models at the census tract level, and evaluates their predictive capabilities. First, we use geographical random forest (GRF), a recently proposed nonlinear machine learning regression method that assesses each predictive factor's spatial variation and contribution to physic

SUBMITTER: Lotfata A 

PROVIDER: S-EPMC10241140 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature