Variable selection in social-environmental data: sparse regression and tree ensemble machine learning approaches.

ABSTRACT:

Background

Social-environmental data obtained from the US Census is an important resource for understanding health disparities, but rarely is the full dataset utilized for analysis. A barrier to incorporating the full data is a lack of solid recommendations for variable selection, with researchers often hand-selecting a few variables. Thus, we evaluated the ability of empirical machine learning approaches to identify social-environmental factors having a true association with a health outcome.

Methods

We compared several popular machine learning methods, including penalized regressions (e.g. lasso, elastic net), and tree ensemble methods. Via simulation, we assessed the methods' ability to identify census variables truly associated with binary and continuous outcomes while m

SUBMITTER: Handorf E 

PROVIDER: S-EPMC7727197 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature