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Other Titles
 Survival Prediction of Rats with Hemorrhagic Shocks Using Support Vector Machine 
 장경환  ;  최재림  ;  유태근  ;  권민경  ;  김덕원 
 Journal of Biomedical Engineering Research (의공학회지), Vol.33(1) : 1-7, 2012 
Journal Title
 Journal of Biomedical Engineering Research (의공학회지) 
Issue Date
hemorrhagic shock ; artificial neural network ; support vector machine ; 5-fold cross validation ; survival prediction
Hemorrhagic shock is a common cause of death in emergency rooms. Early diagnosis of hemorrhagic shock makes it possible for physicians to treat patients successfully. Therefore, the purpose of this study was to select an optimal survival prediction model using physiological parameters for the two analyzed periods: two and five minutes before and after the bleeding end. We obtained heart rates, mean arterial pressures, respiration rates and temperatures from 45 rats. These physiological parameters were used for the training and testing data sets of survival prediction models using an artificial neural network (ANN) and support vector machine (SVM). We applied a 5-fold cross validation method to avoid over-fitting and to select the optimal survival prediction model. In conclusion, SVM model showed slightly better accuracy than ANN model for survival prediction during the entire analysis period.
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Kim, Deok Won(김덕원)
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