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http://dx.doi.org/10.9718/JBER.2012.33.1.001

Survival Prediction of Rats with Hemorrhagic Shocks Using Support Vector Machine  

Jang, K.H. (Graduate Program in Biomedical Engineering, Yonsei University)
Choi, J.L. (Graduate Program in Biomedical Engineering, Yonsei University)
Yoo, T.K. (Department of Medicine, Yonsei University College of Medicine)
Kwon, M.K. (Department of Medical Engineering, Yonsei University College of Medicine)
Kim, D.W. (Graduate Program in Biomedical Engineering, Yonsei University)
Publication Information
Journal of Biomedical Engineering Research / v.33, no.1, 2012 , pp. 1-7 More about this Journal
Abstract
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.
Keywords
hemorrhagic shock; artificial neural network; support vector machine; 5-fold cross validation; survival prediction;
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