County-level Geographic Distributions of Diabetes in Relation to Multiple Factors in the United States

Abstract

The increasing prevalence of diagnosed diabetes has drawn attention of researchers in recent years. In this study, a feature selection method based on linear regression has been used to identify the most relevant factors that are associated with diabetes prevalence from the national county health ranking data sets. Then, Expectation-Maximization clustering algorithm has been used to identify the geo-clusters of counties based on the factors and their relations to the diabetes prevalence for years from 2014 to 2017. The results have identified the unique county-level geographic disparities and trends in diabetes and the related factors over the past four years.