C5.0决策树与RBF神经网络模型用于急性缺血性脑卒中出血性转化的风险预测性能比较

目的  比较C5.0决策树与径向基函数（radial basis function，RBF）神经网络用于急性缺血性脑卒中（acute jschemic stroke，AIS）出血性转化（hemorrhagic transformation，HT）风险预测性能。 方法  将AIS住院患者作为研究对象，收集相关资料。根据入院2周内是否发生HT分为HT组与非HT组，建立C5.0决策树与RBF神经网络模型，比较两者的预测性能。 结果  共收集460份病历资料，按照训练集与测试集7 ∶3的比例分为训练集样本和测试集样本。C5.0决策树模型的训练集与测试集准确率分别为96.5%和80.1%，灵敏度为98.1%和82.6%，特异度为94.8%和77.9%，Kappa指数是0.93和0.60，AUC是0.97和0.80。RBF神经网络模型的训练集与测试集准确率分别为72.6%和74.7%，灵敏度为87.6%和88.4%，特异度为56.9%和62.3%，Kappa指数为0.45和0.50，AUC为0.72和0.75；在训练集中，C5.0决策树模型的预测性能优于RBF神经网络模型的预测性能。在测试集中，两者预测性能的差异无统计学意义。 结论  C5.0决策树模型的预测性能优于RBF神经网络模型的预测性能。

Comparing performance of C5.0 decision tree and radial basis function neural network for predicting hemorrhagic transformation in patients with acute ischemic stroke

  Objective  To compare performance of C5.0 decision tree models and radial basis function(RBF) neural network in predicting the risk of hemorrhagic transformation in acute ischemic stroke.  Methods  Patients with acute ischemic stroke admitted to hospital were enrolled. Hemorrhagic transformation group and non-hemorrhagic transformation group were divided according to whether hemorrhagic transformation occurred within 2 weeks after admission. Retrospectively collected patients' case information. C5.0 decision tree models and RBF neural network model were established with the ratio of 7:3 for training set and test set, and the prediction performance of the model was compared.  Results  A total of 460 patients' case information were collected and divided in 314 training set samples and 146 test set samples. Accuracy rates of the C5.0 decision tree model were 96.5% and 80.1%, sensitivities were 98.1% and 82.6%, specificities were 94.8% and 77.9%, Kappa index were 0.93 and 0.60, and AUC were 0.97 and 0.80. Accuracy rates of the neural network model were 72.6% and 74.7%, sensitivities were 87.6% and 88.4%, specificities were 56.9% and 62.3%, Kappa index were 0.45 and 0.50, and AUCs were 0.72 and 0.75. In the training set, the prediction performance of the C5.0 decision tree model was superior to the RBF neural network model. However, there was no statistical difference in the test set.  Conclusion  C5.0 decision tree model is better than RBF neural network model in risk prediction.