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dc.contributor.advisorSubasi, Ersoy
dc.contributor.authorMoreno, Megan
dc.contributor.authorMoreno Cole, Melissa Megan
dc.date.accessioned2021-06-18T14:54:37Z
dc.date.available2021-06-18T14:54:37Z
dc.date.created2021-05
dc.date.issued2021-05
dc.date.submittedMay 2021
dc.identifier.urihttp://hdl.handle.net/11141/3366
dc.descriptionThesis (M.S.) - Florida Institute of Technology, 2021.en_US
dc.description.abstractWe apply a pattern-based classification method to identify clinical and genomic features associated with the progression of Chronic Kidney Disease (CKD). We analyze the African-American Study of Chronic Kidney Disease with Hypertension (AASK) dataset and construct a decision-tree classification model, consisting15 combinatorial patterns of clinical features and single nucleotide polymorphisms (SNPs), seven of which are associated with slow progression and eight with rapid progression of renal disease among AASK patients. We identify four clinical features and two SNPs that can accurately predict CKD progression. These features are validated with using sophisticated machine learning techniques including Random Forest, Nearest Neighbor, Support Vector Machines, Neural Networks, Logistic Regression, and Naive Bayes supervised learning methods. Clinical and genomic features identified in our experiments may be used in a future study to develop new therapeutic interventions for CKD patients.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCopyright held by author.en_US
dc.titlePattern Based Classification of Chronic Kidney Disease Patientsen_US
dc.typeThesisen_US
dc.date.updated2021-06-18T13:56:29Z
thesis.degree.nameMaster of Science in Operations Researchen_US
thesis.degree.levelMastersen_US
thesis.degree.disciplineOperations Researchen_US
thesis.degree.departmentMathematical Sciencesen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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