CAJ | 학술논문

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。
본문제출료대유선암적지식진행알굴급대기유효적망락여이탐토。재응용각충수거알굴방법지전，이용소개발적망락조출병정발전적개솔。유관결과유조우의생침대병인적병정진행합리치료。위극복수거집적고유도문제병실현수거지간적관련성，본문채용주성분분석법래강저수거유도병조출괄용적망락。운용 BP 신경망락결구진행료평고，대북경모의원적유선암수거방면BP신경망락적성능진행료분석。결과표명주성분분석소제료망락수입지간적상관성，강저료망락적수입층수，개선종정체상제고료망락적성능。최종취득료량호적예측결과。
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.