【摘要】 本文提出了对乳腺癌的知识进行挖掘及对其有效的网络予以探讨.在应用各种数据挖掘方法之前,利用所开发的网络找出病情发展的概率.有关结果有助于医生针对病人的病情进行合理治疗.为克服数据集的高维度问题并实现数据之间的关联性,本文采用主成分分析法来降低数据维度并找出适用的网络.运用BP神经网络结构进行了评估,对北京某医院的乳腺癌数据方面BP神经网络的性能进行了分析.结果表明主成分分析消除了网络输入之间的相关性,降低了网络的输入层数,改善从整体上提高了网络的性能.最终取得了良好的预测结果.更多还原

【Abstract】 This paper presents on breast cancer knowledge mining and network to its effective to explore. Before the application of various data mining method in probability, find out the progression of the disease by using the network. The results are helpful to reasonable treatment to the doctor for the patient. For the high dimension data set and overcome the problems associated with the data, this paper uses principal component to reduce the data dimension and find out the suitable network analysis. Application of BP neural network structure is assessed, performance on the breast cancer data in a hospital in Beijing, the BP neural network is analyzed. The results show that principal component analysis to eliminate the correlation between the input of the network, reducing the input of network, improve the performance of the network as a whole. The good results have been achieved.更多还原