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출혈성 쇼크를 일으킨 흰쥐에서 인공신경망과 지원벡터기계를 이용한 생존율 비교

Authors
 장경환 ; 유태근 ; 김덕원 ; 권민경 ; 최재림 ; 남기창 
Citation
 Journal of The Institute of Electronics Engineers of Korea (전자공학회논문지 - SC), Vol.48(2) : 107~115, 2011 
Journal Title
 Journal of The Institute of Electronics Engineers of Korea (전자공학회논문지 - SC) 
ISSN
 1229-6392 
Issue Date
2011
Abstract
shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.
URI
http://ir.ymlib.yonsei.ac.kr/handle/22282913/93332
Appears in Collections:
1. 연구논문 > 1. College of Medicine > Dept. of Medical Engineering
Yonsei Authors
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