Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation

Wu Guan-peng; Wang Shuai; Huang Wei; Liu Tong-hai; Yin Yong; Liu Yi-hui

doi:doi:10.11648/j.jeee.20160402.16

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Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation

Wu Guan-peng, Wang Shuai, Huang Wei, Liu Tong-hai, Yin Yong, Liu Yi-hui

Published in Journal of Electrical and Electronic Engineering (Volume 4, Issue 2)

Received: 12 April 2016 Published: 13 April 2016

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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.

Published in	Journal of Electrical and Electronic Engineering (Volume 4, Issue 2)
DOI	10.11648/j.jeee.20160402.16
Page(s)	35-39
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Primary Liver Carcinoma, HBV Reactivation, Feature Extraction, BP, RBF

References

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APA Style

Wu Guan-peng, Wang Shuai, Huang Wei, Liu Tong-hai, Yin Yong, et al. (2016). Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation. Journal of Electrical and Electronic Engineering, 4(2), 35-39. https://doi.org/10.11648/j.jeee.20160402.16

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ACS Style

Wu Guan-peng; Wang Shuai; Huang Wei; Liu Tong-hai; Yin Yong, et al. Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation. J. Electr. Electron. Eng. 2016, 4(2), 35-39. doi: 10.11648/j.jeee.20160402.16

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AMA Style

Wu Guan-peng, Wang Shuai, Huang Wei, Liu Tong-hai, Yin Yong, et al. Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation. J Electr Electron Eng. 2016;4(2):35-39. doi: 10.11648/j.jeee.20160402.16

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@article{10.11648/j.jeee.20160402.16,
  author = {Wu Guan-peng and Wang Shuai and Huang Wei and Liu Tong-hai and Yin Yong and Liu Yi-hui},
  title = {Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {4},
  number = {2},
  pages = {35-39},
  doi = {10.11648/j.jeee.20160402.16},
  url = {https://doi.org/10.11648/j.jeee.20160402.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20160402.16},
  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.},
 year = {2016}
}

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TY  - JOUR
T1  - Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation
AU  - Wu Guan-peng
AU  - Wang Shuai
AU  - Huang Wei
AU  - Liu Tong-hai
AU  - Yin Yong
AU  - Liu Yi-hui
Y1  - 2016/04/13
PY  - 2016
N1  - https://doi.org/10.11648/j.jeee.20160402.16
DO  - 10.11648/j.jeee.20160402.16
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 35
EP  - 39
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20160402.16
AB  - 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.
VL  - 4
IS  - 2
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Wu Guan-peng, Wang Shuai, Huang Wei, Liu Tong-hai, Yin Yong, et al. (2016). Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation. Journal of Electrical and Electronic Engineering, 4(2), 35-39. https://doi.org/10.11648/j.jeee.20160402.16

Copy | Download

ACS Style

Wu Guan-peng; Wang Shuai; Huang Wei; Liu Tong-hai; Yin Yong, et al. Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation. J. Electr. Electron. Eng. 2016, 4(2), 35-39. doi: 10.11648/j.jeee.20160402.16

Copy | Download

AMA Style

Wu Guan-peng, Wang Shuai, Huang Wei, Liu Tong-hai, Yin Yong, et al. Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation. J Electr Electron Eng. 2016;4(2):35-39. doi: 10.11648/j.jeee.20160402.16

Copy | Download

@article{10.11648/j.jeee.20160402.16,
  author = {Wu Guan-peng and Wang Shuai and Huang Wei and Liu Tong-hai and Yin Yong and Liu Yi-hui},
  title = {Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {4},
  number = {2},
  pages = {35-39},
  doi = {10.11648/j.jeee.20160402.16},
  url = {https://doi.org/10.11648/j.jeee.20160402.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20160402.16},
  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.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Application of BP and RBF Neural Network in Classification Prognosis of Hepatitis B Virus Reactivation
AU  - Wu Guan-peng
AU  - Wang Shuai
AU  - Huang Wei
AU  - Liu Tong-hai
AU  - Yin Yong
AU  - Liu Yi-hui
Y1  - 2016/04/13
PY  - 2016
N1  - https://doi.org/10.11648/j.jeee.20160402.16
DO  - 10.11648/j.jeee.20160402.16
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 35
EP  - 39
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20160402.16
AB  - 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.
VL  - 4
IS  - 2
ER  -

Copy | Download