한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제17권10호
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  • Pages.145-154
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  • 2012
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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ST 분절 급상승 심근경색 환자들의 단기 재발 사망 예측

Short-term Mortality Prediction of Recurrence Patients with ST-segment Elevation Myocardial Infarction

  • 임광현 ((주)에이텍 시스템사업부) ;
  • 류광선 (충북대학교 컴퓨터과학과) ;
  • 박수호 (충북대학교 컴퓨터과학과) ;
  • 손호선 (충북대학교 컴퓨터과학과) ;
  • 류근호 (충북대학교 전자계산학과)
  • Lim, Kwang-Hyeon (System Business DEPT. ATEC Co., Ltd.) ;
  • Ryu, Kwang-Sun (School of Computer Science, Chungbuk National University) ;
  • Park, Soo-Ho (School of Computer Science, Chungbuk National University) ;
  • Shon, Ho-Sun (School of Computer Science, Chungbuk National University) ;
  • Ryu, Keun-Ho (School of Electronic and Computer Engineering, Chungbuk National University)
  • 투고 : 2012.06.01
  • 심사 : 2012.08.31
  • 발행 : 2012.10.31
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초록

현대 사회는 서구화된 식생활 패턴과 흡연, 비만 등의 원인으로 인해 심혈관계 질환들이 급증하고 있다. 특히, 급성심근경색은 심혈관계 질환으로 인한 사망의 대부분을 차지하고 있다. 이러한 추세에 따라 해외 선진국에서는 임상생리학적 오류를 줄이기 위해서 자국민의 데이터를 기반으로 급성심근경색의 발병 및 질병에 영향을 미치는 위험인자를 찾는 연구가 활발히 진행되고 있다. 하지만 한국인에 적합한 급성심근경색 예후 진단 예측 시스템이 미비한 실정이다. 따라서 이 논문에서는 KAMIR(Korea Acute Myocardial Infarction Registry) 데이터베이스에서 제공 받은 급성심근경색 환자의 예후 데이터를 기반으로 ST분절 급상승 심근경색 재발 환자들의 단기 사망률 예측모델을 찾고자 한다. 실험을 통해 로지스틱 회귀 분석에 의해 추출된 속성 집합을 적용하였을 때 기존의 원시 데이터 보다 높은 정확도를 얻을 수 있었으며, 인공신경망의 경우 다른 분류기법들보다 높은 성능을 보였다. 이를 통해 ST 분절 급상승 심근경색 재발 환자들의 단기 사망률을 예측함으로써 향후 고위험군 환자들의 관리에 도움을 줄 수 있을 것으로 기대한다.

Recently, the cardiovascular disease has increased by causes such as westernization dietary life, smoking, and obesity. In particular, the acute myocardial infarction (AMI) occupies 50% death rate in cardiovascular disease. Following this trend, the AMI has been carried out a research for discovery of risk factors based on national data. However, there is a lack of diagnosis minor suitable for Korean. The objective of this paper is to develop a classifier for short-term relapse mortality prediction of cardiovascular disease patient based on prognosis data which is supported by KAMIR(Korea Acute Myocardial Infarction). Through this study, we came to a conclusion that ANN is the most suitable method for predicting the short-term relapse mortality of patients who have ST-segment elevation myocardial infarction. Also, data set obtained by logistic regression analysis performed highly efficient performance than existing data set. So, it is expect to contribute to prognosis estimation through proper classification of high-risk patients.

키워드

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