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

dc.contributor.authorOraz, Gulzhahan
dc.contributor.authorLuo, Xiao
dc.contributor.departmentComputer and Information Science, School of Scienceen_US
dc.date.accessioned2019-03-28T17:22:45Z
dc.date.available2019-03-28T17:22:45Z
dc.date.issued2018-03
dc.description.abstractThe 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.en_US
dc.eprint.versionAuthor's manuscripten_US
dc.identifier.citationOraz, G., & Luo, X. (2018). County-level geographic distributions of diabetes in relation to multiple factors in the united states. In 2018 IEEE EMBS International Conference on Biomedical Health Informatics (BHI) (pp. 279–282). https://doi.org/10.1109/BHI.2018.8333423en_US
dc.identifier.urihttps://hdl.handle.net/1805/18702
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/BHI.2018.8333423en_US
dc.relation.journal2018 IEEE EMBS International Conference on Biomedical Health Informaticsen_US
dc.rightsPublisher Policyen_US
dc.sourceAuthoren_US
dc.subjectdiabetesen_US
dc.subjectfeature extractionen_US
dc.subjectobesityen_US
dc.titleCounty-level Geographic Distributions of Diabetes in Relation to Multiple Factors in the United Statesen_US
dc.typeConference proceedingsen_US
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