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Comparison of Survival Prediction of Rats with Hemorrhagic Shocks Using Artificial Neural Network and Support Vector Machine  

Jang, Kyung-Hwan (Graduate Program in Biomedical Engineering, Yonsei University)
Yoo, Tae-Keun (Yonsei Uinversity College of Medicine)
Nam, Ki-Chang (Clinical Trials Center for Medical Devices, Severance Hospital)
Choi, Jae-Rim (Graduate Program in Biomedical Engineering, Yonsei University)
Kwon, Min-Kyung (Brain Korea 21 Project for Medical Science, Yonsei University)
Kim, Deok-Won (Dept. of Medical Engineering, Yonsei Uinversity College of Medicine)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.48, no.2, 2011 , pp. 47-55 More about this Journal
Abstract
Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic 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.
Keywords
hemorrhagic shock; artificial neural network; support vector machine; rats; survival prediction;
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