2023
DOI: 10.1038/s41598-023-30188-9
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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… Show more

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Cited by 12 publications
(22 citation statements)
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“…Moreover, use of tree-based ML approaches such as CART provides a practical representation of the intricate web of SEDH and geographic variation in obesity prevalence. 10,12 This study has limitations. Newer research suggests that BMI is not an adequate surrogate for visceral adiposity.…”
Section: Discussionmentioning
confidence: 95%
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“…Moreover, use of tree-based ML approaches such as CART provides a practical representation of the intricate web of SEDH and geographic variation in obesity prevalence. 10,12 This study has limitations. Newer research suggests that BMI is not an adequate surrogate for visceral adiposity.…”
Section: Discussionmentioning
confidence: 95%
“…CART is capable of sequentially divide data into smaller, homogeneous groups using binary conditional inferences (“if-then” rules). 12 To control the splitting of data, we adopted the stopping criteria of a statistical significance (p < 0.05) at each branching point obtained by Pearson’s correlation test, a maximum of depth of 6 splits and a minimum of 150 counties in each terminal node. We then labeled the terminal nodes, or county clusters, with alphabet letters from lowest to highest median obesity prevalence.…”
Section: Methodsmentioning
confidence: 99%
“…Random Forest, being a black-box model, yields less explainable results regarding how and why a variable becomes less or more important, while CART excels in visualizing the 'pathway' of each observation with statistical significance at decision points or splits. This novel combination of tools has been demonstrated successfully in prior studies investigating late-stage breast cancer diagnosis, 24 premature cardiovascular mortality, 26 and Alzheimer's disease mortality. 56 Our study further demonstrates its utility in understanding obesity prevalence.…”
Section: Discussionmentioning
confidence: 96%
“…Our study demonstrates that other SEDH‐related factors can aid in the identification of areas with higher obesity prevalence, and therefore, deserve more attention in future investigations. Moreover, the use of tree‐based machine‐learning approaches, such as the CART method, provides a practical representation of the intricate web of SEDH and geographic variation in obesity prevalence 24,26 . Analyses such as these can inform the development of obesity prevention and treatment strategies tailored to the unique characteristics of a region, which optimizes the likelihood of local adoption of such strategies and ultimately plays an essential role in reducing obesity‐related chronic diseases.…”
Section: Discussionmentioning
confidence: 99%
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