title: Comparison of survival predictions for rats with hemorrhagic shocks using an artificial neural network and support vector machine.
abstract: Hemorrhagic shock is the cause of one third of deaths resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physicians to treat patients successfully. The objective of this study was to select an optimal survival prediction model using physiological parameters from rats during our hemorrhagic experiment. These physiological parameters were used for the training and testing of survival prediction models using an artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the optimal survival prediction model according to performance measured by a 5-fold cross validation method. We selected an ANN with three hidden neurons and one hidden layer and an SVM with Gaussian kernel function as a trained survival prediction model. For the ANN model, the sensitivity, specificity, and accuracy of survival prediction were 97.8 ± 3.3 %, 96.3 ± 2.7 %, and 96.8 ± 1.7 %, respectively. For the SVM model, the sensitivity, specificity, and accuracy were 97.5 ± 2.9 %, 99.3 ± 1.1 %, and 98.5 ± 1.2 %, respectively. SVM was preferable to ANN for the survival prediction.
authors.affiliation: Graduate Program in Biomedical Engineering, Yonsei University, Seoul, Korea. khjang@yuhs.ac
authors.forename: Kyung Hwan
authors.initials: KH
authors.lastname: Jang
authors.forename: Tae Keun
authors.initials: TK
authors.lastname: Yoo
authors.forename: Joon Yul
authors.initials: JY
authors.lastname: Choi
authors.forename: Ki Chang
authors.initials: KC
authors.lastname: Nam
authors.forename: Jae Lim
authors.initials: JL
authors.lastname: Choi
authors.forename: Min Kyung
authors.initials: MK
authors.lastname: Kwon
authors.forename: Deok Won
authors.initials: DW
authors.lastname: Kim
mesh_terms: D000818:Animals; D042241:Early Diagnosis; D015994:Incidence; D008297:Male; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis; D016016:Proportional Hazards Models; D051381:Rats; D017207:Rats, Sprague-Dawley; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012771:Shock, Hemorrhagic / Q000401:mortality*; D060388:Support Vector Machine; D016019:Survival Analysis; D015996:Survival Rate