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
Views 4096      Downloads 149
Authors
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
Article Tools
Follow on us
Abstract
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.
Keywords
Primary Liver Carcinoma, HBV Reactivation, Feature Extraction, BP, RBF
To cite this article
Wu Guan-peng, Wang Shuai, Huang Wei, Liu Tong-hai, Yin Yong, Liu Yi-hui, 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. doi: 10.11648/j.jeee.20160402.16
References
[1]
Yeo W, Lam KC, Zee B, et al. Hepatitis B reactivation in patients with hepatocellular carcinoma undergoing systemic chemotherapy [J]. Annals of Oncology, 2004, 15(7): 1661-1666.
[2]
Kim JH, Park J W, Kim TH, et al. Hepatitis B virus reactivation after three-dimensional conformal radiotherapy in patients with hepatitis B virus-related hepatocellular carcinoma [J]. International Journal of Radiation Oncology Biology Physics, 2007, 69(3): 813–819.
[3]
Xiao-An W U, Zhang Z Y, Hong J Y. Therapeutic Effect of Three-Dimensional Conformal Radiotherapy in the Treatment of Primary Hepatoellular Carcinoma [J]. Practical Journal of Cancer, 2007.
[4]
Xiao-An W U, Zhang Z Y, Yan Z B, et al. Clinical study of three-dimensional conformal radiation therapy for primary hepatoellular carcinoma [J]. Chinese Clinical Oncology, 2008.
[5]
Wei H, Yanda L, Min F, et al. Analysis of hepatitis B virus reactivation inducedby precise radiotherapy in patients with primary liver cancer [J]. Chinese Journal of Radiation Oncology, 2013, 22(3): 193-197.
[6]
Wei H, Wei Z, Min F, et al. Risk factors for hepatitis B virus reactivation after conformal radiotherapy in patients with hepatocellular carcinoma. [J]. Cancer Science, 2014, 105(6): 697-703.
[7]
Liu Y H. Feature extraction and dimensionality reduction for mass spectrometry data [A]. Computers in Biology and Medicine, 2009, 39: 818-823.
[8]
Liu Y, Bai L. Find Key m/z Values in Predication of Mass Spectrometry Cancer Data [C]// Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, 4th International Conference on Intelligent Computing, ICIC 2008, Shanghai, China, September 15-18, 2008, Proceedings. 2008:196-203.
[9]
Agarwal M, Jain N, Kumar M, H Agrawal. Face Recognition Using Eigen Faces and Artificial Neural Network [C]// IEEE Computer Society Conference on Computer Vision & Pattern Recognition, Cvpr. 2010:624-629.
[10]
Scanzio S, Cumani S, Gemello R, F Mana, P Laface. Parallel implementation of Artificial Neural Network training for speech recognition [J]. Pattern Recognition Letters, 2010, 31(11):1302-1309.
[11]
Yang Z, Zhang H. Prediction of melt rate of vibrating-electrode Electroslag Remelting process using artificial neural network [C]// Information Science and Technology (ICIST), 2015 5th International Conference on. IEEE, 2015.
[12]
Liu M, Tsai H P. USB3.1 silicon and channel design optimization using artificial neural network modeling [C]// Electromagnetic Compatibility and Signal Integrity, 2015 IEEE Symposium on. IEEE, 2015.
[13]
Wang S Q, Liu Y H, Wang L J, et al. ~(31)P-MRS data analysis of liver based on back-propagation neural networks [J]. Chinese Journal of Medical Imaging Technology, 2009, 25(10):1875-1878.
[14]
Qiang L, Liu Y H, Wang L J, JY Cheng. ~(31)P-MRS data analysis of liver based on self-organizing map neural networks [J]. Journal of Medical Imaging, 2009, 2:151-153.
[15]
Liu Q, Wang N, Liu Y H, et al. 31P MRS Data Analysis of Liver Cancer Based on Neural Networks [J]. Applied Mechanics & Materials, 2010, 20:630-635.
[16]
Farrell-Singleton P A. Critical values for the two independent samples Winsorized t test[J]. Dissertations & Theses - Gradworks, 2010.
[17]
Mchugh M L. The chi-square test of independence. [J]. Biochemia Medica, 2013, 23(2): 143-9.
[18]
Murakami H. The power of the modified Wilcoxon rank-sum test for the one-sided alternative [J]. Statistics A Journal of Theoretical & Applied Statistics, 2014, 49(4):1-14.
[19]
Yao H, Gong J, Li L I, Do Radiotherapy, SS Hospital. Risk Factors of Hepatitis B Virus Reactivation Induced by Precise Radiotherapy in Patients with Hepatic Carcinoma [J]. Practical Journal of Cancer, 2014.
[20]
Li X, Liu S, Hui L, T Zhang. Research on Prediction Model of Bending Force Based on BP Neural Network with LM Algorithm [C]// the 25th Chinese Control and Decision Conference. 2013:832-836.
[21]
Han H G, Qiao J F. Adaptive Computation Algorithm for RBF Neural Network [J]. IEEE Transactions on Neural Networks & Learning Systems, 2012, 23(2):342-347.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186