Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach.

ABSTRACT: Disparities in premature cardiovascular mortality (PCVM) have been associated with socioeconomic, behavioral, and environmental risk factors. Understanding the "phenotypes", or combinations of characteristics associated with the highest risk of PCVM, and the geographic distributions of these phenotypes is critical to targeting PCVM interventions. This study applied the classification and regression tree (CART) to identify county phenotypes of PCVM and geographic information systems to examine the distributions of identified phenotypes. Random forest analysis was applied to evaluate the relative importance of risk factors associated with PCVM. The CART analysis identified seven county phenotypes of PCVM, where high-risk phenotypes were characterized by having greater percentages of people w

SUBMITTER: Dong W 

PROVIDER: S-EPMC9941082 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature