- Volume 14 Issue 7
DOI QR Code
Developing data quality management algorithm for Hypertension Patients accompanied with Diabetes Mellitus By Data Mining
데이터 마이닝을 이용한 고혈압환자의 당뇨질환 동반에 관한 데이터 질 관리 알고리즘 개발
- Hwang, Kyu-Yeon (Pusan National University Hospital) ;
- Lee, Eun-Sook (Pusan National University Hospital) ;
- Kim, Go-Won (Pusan National University Hospital) ;
- Hong, Sung-Ok (Korea Centers for Disease Control and Prevention) ;
- Park, Jong-Son (Korea Health Industry Development Institute) ;
- Kwak, Mi-Sook (Korea Health Industry Development Institute) ;
- Lee, Ye-Jin (Korea Health Industry Development Institute) ;
- Im, Chae-Hyuk (Dept. of Health Policy & Management, InJe University) ;
- Park, Tae-Hyun (Dept. of Health Policy & Management, InJe University) ;
- Park, Jong-Ho (Dept. of Health Policy & Management, InJe University) ;
- Kang, Sung-Hong (Dept. of Health Policy & Management, InJe University)
- 황규연 (부산대학교병원) ;
- 이은숙 (부산대학교병원) ;
- 김고원 (부산대학교병원) ;
- 홍성옥 (질병관리본부) ;
- 박정선 (한국보건산업진흥원) ;
- 곽미숙 (한국보건산업진흥원) ;
- 이예진 (한국보건산업진흥원) ;
- 임채혁 (인제대학교 보건행정학과) ;
- 박태현 (인제대학교 보건행정학과) ;
- 박종호 (인제대학교 보건행정학과) ;
- 강성홍 (인제대학교 보건행정학과)
- Received : 2016.06.01
- Accepted : 2016.07.20
- Published : 2016.07.28
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.
Data Mining;Data Quality Management Algorithm;Outlier Detection Method;Hypertension;Diabetes Mellitus
Grant : 융복합보건의료기술
Supported by : 한국보건산업진흥원
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