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Developing data quality management algorithm for Hypertension Patients accompanied with Diabetes Mellitus By Data Mining
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  • Journal title : Journal of Digital Convergence
  • Volume 14, Issue 7,  2016, pp.309-319
  • Publisher : The Society of Digital Policy and Management
  • DOI : 10.14400/JDC.2016.14.7.309
 Title & Authors
Developing data quality management algorithm for Hypertension Patients accompanied with Diabetes Mellitus By Data Mining
Hwang, Kyu-Yeon; Lee, Eun-Sook; Kim, Go-Won; Hong, Sung-Ok; Park, Jong-Son; Kwak, Mi-Sook; Lee, Ye-Jin; Im, Chae-Hyuk; Park, Tae-Hyun; Park, Jong-Ho; Kang, Sung-Hong;
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 Abstract
There is a need to develop a data quality management algorithm in order to improve the quality of health care data. In this study, we developed a data quality control algorithms associated diseases related to diabetes in patients with hypertension. To make a data quality algorithm, we extracted hypertension patients from 2011 and 2012 discharge damage survey data. As the result of developing Data quality management algorithm, significant factors in hypertension patients with diabetes are gender, age, Glomerular disorders in diabetes mellitus, Diabetic retinopathy, Diabetic polyneuropathy, Closed [percutaneous] [needle] biopsy of kidney. Depending on the decision tree results, we defined Outlier which was probability values associated with a patient having diabetes corporal with hypertension or more than 80%, or not more than 20%, and found six groups with extreme values for diabetes accompanying hypertension patients. Thus there is a need to check the actual data contained in the Outlier(extreme value) groups to improve the quality of the data.
 Keywords
Data Mining;Data Quality Management Algorithm;Outlier Detection Method;Hypertension;Diabetes Mellitus;
 Language
Korean
 Cited by
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