사례기반추론을 이용한 대용량 데이터의 실시간 처리 방법론

• 영문명: Data Mining Approach for Real-Time Processing of Large Data Using Case-Based Reasoning : High-Risk Group Detection Data Warehouse for Patients with High Blood Pressure
• 발행기관: 한국IT서비스학회
• 저자명: 박성혁(Sung-Hyuk Park) 양근우(Kun-Woo Yang)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제10권 제1호, 135~149쪽, 전체 15쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2011.03.30

영문 초록

In this paper, we propose the high-risk group detection model for patients with high blood pressure using case-based reasoning. The proposed model can be applied for public health maintenance organizations to effectively manage knowledge related to high blood pressure and efficiently allocate limited health care resources. Especially, the focus is on the development of the model that can handle constraints such as managing large volume of data, enabling the automatic learning to adapt to external environmental changes and operating the system on a real-time basis. Using real data collected from local public health centers, the optimal high-risk group detection model was derived incorporating optimal parameter sets. The results of the performance test for the model using test data show that the prediction accuracy of the proposed model is two times better than the natural risk of high blood pressure.

목차

1. 서론

2. 관련 연구

3. 고혈압 고위험군 탐색을 위한 자동학습 데이터 웨어하우스

4. 실험

5. 결론

키워드

Clinical Decision Support System High Blood Pressure Data Mining CBR