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发表时间：2018-01-01

发表刊物：管理科学

卷号：31

期号：1

页面范围：33-47

ISSN号：1672-0334

摘要：Prostate cancer is the fastest rising incidence of male cancer in recent years,which is a serious health threat to the pa-tients.How to diagnose the condition of cancer patients more accurately is very important for the timely treatment and reduction of the mortality of pros-tate cancer.In recent years,cancer diagnosis based on data mining has gradually become a research focus in the field of disease diagnosis,and it has shown great advantages in improving the accuracy of diagnosis. In order to solve the problem that the low accuracy of the existing methods for early diagnosis of prostate cancer,this paper presents a new diagnosis method called GMM-RBF neural network based on improved RBF neural network with GMM.In this method,the parameters of radial basis function in radial basis function neural network are pre -trained by using Gaussian mixture model to avoid the model getting into local optimum.Then,the improved PSO algorithm is used to train the neural network.In the experiment,the data provided by the National Clinical Medical Science Data Center is used to compare the proposed method with the other popular machine learning methods such as RBF neural network,classification and regression tree,support vector machine and logistic regression.The performance of the model is evaluated using accuracy,specificity,sensitivity,and AUC. The experimental results show that the GMM-RBF neural network model has faster convergence rate and higher initial accu -racy than the pre-improved neural network model.Compared with other machine learning algorithms,the GMM-RBF neural net-work model achieves a higher accuracy,sensitivity,specificity and AUC during ten-fold cross-validation. In this paper, the proposed GMM-RBF neural network method has a great improvement on the model prediction accuracy compared with the traditional RBF neural network model, which can provide more reliable results for the diagnosis of prostate cancer.It provides effective auxiliary decision-making support for the preliminary diagnosis of prostate cancer for medical workers and has practical significance to reduce the pain of patients,improve patient satisfaction and save medical resources.

备注：新增回溯数据