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Hemorrhagic shock is the cause of one third of deaths resulting from injury in the world. Although many studies have tried to diagnose hemorrhagic shock early and accurately, such attempts were inconclusive due to compensatory mechanisms of humans. The objective of this study was to construct a survival prediction model of rats in hemorrhagic shock using a random forest (RF) model, which is a newly emerged classifier acknowledged for its performance. Heart rate (HR), mean arterial pressure (MAP), respiratory rate (RR), lactate concentration (LC), and perfusion (PF) measured in rats were used as input variables for the RF model and its performance was compared with that of a logistic regression (LR) model. Before constructing the models, we performed a 5-fold cross validation for RF variable selection and forward stepwise variable selection for the LR model to see which variables are important for the models. For the LR model, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (ROC-AUC) were 1, 0.89, 0.94, and 0.98, respectively. For the RF models, sensitivity, specificity, accuracy, and AUC were 0.96, 1, 0.98, and 0.99, respectively. In conclusion, the RF model was superior to the LR model for survival prediction in the rat model.