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Comparison of survival predictions for rats with hemorrhagic shocks using an artificial neural network and support vector machine

机译:使用人工神经网络对出血冲击大鼠生存预测的比较,支持矢量机

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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.
机译:出血休克是世界伤害导致的三分之一的原因。早期诊断出血休克使医生能够成功治疗患者。本研究的目的是使用从我们出血试验期间使用来自大鼠的生理参数的最佳存活预测模型。这些生理参数用于使用人工神经网络(ANN)并支持向量机(SVM)训练和测试生存预测模型。为避免过度拟合,我们根据通过5倍交叉验证方法测量的性能选择最佳生存预测模型。我们选择了一个具有三个隐藏神经元和一个隐藏层的ANN,以及具有高斯内核功能的SVM作为训练的生存预测模型。对于ANN模型,存活预测的敏感性,特异性和准确性分别为97.8±3.3%,96.3±2.7%和96.8±1.7%。对于SVM模型,敏感性,特异性和准确性分别为97.5±2.9%,99.3±1.1%和98.5±1.2%。对于生存预测,SVM优选为ANN。

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