Artificial neural networks, ; Support vector machines, ; Predictive models, ; Hemorrhaging, ; Electric shock, ; Accuracy, ; Neurons
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.