Machine learning to compare frequent medical problems of African American and caucasian diabetic kidney patients

Yong Mi Kim, Pranay Kathuria, Dursun Delen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Objectives: End-stage renal disease (ESRD), which is primarily a consequence of diabetes mellitus, shows an exemplary health disparity between African American and Caucasian patients in the United States. Because diabetic chronic kidney disease (CKD) patients of these two groups show differences in their medical problems, the markers leading to ESRD are also expected to differ. The purpose of this study was, therefore, to compare their medical complications at various levels of kidney function and to identify markers that can be used to predict ESRD. Methods: The data of type 2 diabetic patients was obtained from the 2012 Cerner database, which totaled 1,038,499 records. The data was then filtered to include only African American and Caucasian outpatients with estimated glomerular filtration rates (eGFR), leaving 4,623 records. A priori machine learning was used to discover frequently appearing medical problems within the filtered data. CKD is defined as abnormalities of kidney structure, present for >3 months. Results: This study found that African Americans have much higher rates of CKDrelated medical problems than Caucasians for all five stages, and prominent markers leading to ESRD were discovered only for the African American group. These markers are high glucose, high systolic blood pressure (BP), obesity, alcohol/drug use, and low hematocrit. Additionally, the roles of systolic BP and diastolic BP vary depending on the CKD stage. Conclusions: This research discovered frequently appearing medical problems across five stages of CKD and further showed that many of the markers reported in previous studies are more applicable to African American patients than Caucasian patients.

Original languageEnglish
Pages (from-to)241-248
Number of pages8
JournalHealthcare Informatics Research
Issue number4
StatePublished - 1 Oct 2017
Externally publishedYes


  • Electronic health records
  • Glomerular filtration rate
  • Kidney failure
  • Machine learning
  • Renal insufficiency


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