Identifying Clinical and Genomic Features Associated With Chronic Kidney Disease

• Main Authors: M. Megan Moreno, Travaughn C. Bain, Melissa S. Moreno, Katherine C. Carroll, Emily R. Cunningham, Zoe Ashton, Roby Poteau, Ersoy Subasi, Michael Lipkowitz, Munevver Mine Subasi
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-01-01
• Series: Frontiers in Big Data
• Subjects: classification; genomic analysis; AASK; chronic kidney disease; decision trees
• Online Access: https://www.frontiersin.org/articles/10.3389/fdata.2020.528828/full
• ISSN: 2624-909X

We 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 dataset and construct a decision-tree classification model, consisting 15 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 African-American Study of Chronic Kidney patients. We identify four clinical features and two SNPs that can accurately predict CKD progression. Clinical and genomic features identified in our experiments may be used in a future study to develop new therapeutic interventions for CKD patients.