Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation
Journal of Electrical and Electronic Engineering
Volume 4, Issue 2, April 2016, Pages: 35-39
Received: Apr. 12, 2016;
Published: Apr. 13, 2016
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Wu Guan-peng, School of Information, Qilu University of Technology, Jinan, China
Wang Shuai, School of Information, Qilu University of Technology, Jinan, China
Huang Wei, Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China
Liu Tong-hai, Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China
Yin Yong, Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China
Liu Yi-hui, School of Information, Qilu University of Technology, Jinan, China
This study aims at finding the risk factors (the key feature subset) and building the classification prognosis model of hepatitis B virus (HBV) reactivation after precise radiotherapy (RT) in patients with primary liver carcinoma. We find out that the outer margin of RT, TNM of tumor stage and the HBV DNA levels are the risk factors (P<0.05) of HBV reactivation by feature extraction method of logistic regression analysis in this article. The feature extraction method reduced the dimension and improved the classification accuracy. Establish the classification prognosis model of BP and RBF neural network for original data set and the key feature subset. The experimental results show that BP and RBF neural network have good performance in classification of HBV reactivation.
Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation, Journal of Electrical and Electronic Engineering.
Vol. 4, No. 2,
2016, pp. 35-39.
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