3种统计模型在预测肺癌术后并发症中的比较

A comparison between three statistical models in predicting post-operative complication for lung cancer patients

目的探讨BP神经网络模型在预测肺癌术后并发症中的应用价值。方法调查肺癌患者术后并发症发生情况。分别应用Logistic回归、BP神经网络模型和经Logistic回归筛选变量后的BP神经网络模型3种办法建立预测模型,并比较3种模型的预测准确度。结果 Logistic回归、BP神经网络模型和经Logistic回归筛选变量后的BP神经网络模型的预测一致率分别为81.6%、89.7%、90.8%。3种模型受试者工作特征曲线(ROC曲线)下面积(AUC)分别为0.636、0.801、0.808。Logistic模型的AUC与两种BP神经网络模型的差异有统计学意义(P<0.05)。结论 BP神经网络对肺癌术后并发症预测的效果优于Logistic回归模型。

Objective To explore the application value of BP neural network in predicting post-operative complication for lung cancer patients. Methods We applied Logistic regression, BP neural network model and BP neural network model screening variables by Logistic regression to establish prediction models and evaluate the practical application of each model in the prediction accuracy. Results The prediction accuracy of Logistic regression, BP neural network model and BP neural network model screening variables by Logistic regression were 81. 6% , 89. 7% ,90. 8% and the AVe of Roe in the three models were 0. 636,0. 801,0.808, respectively. There were significant differences of the AVe of Roe between Logistic regression and two BP neural network models. Conclusion The discrimination performance of BP neural network models is better than Logistic regression in the prediction of post-operative complication for lung cancer patients.