Journal of Electrical and Electronic Engineering

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

Received: 12 April 2016    Accepted:     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.

DOI 10.11648/j.jeee.20160402.16
Published in Journal of Electrical and Electronic Engineering (Volume 4, Issue 2, April 2016)
Page(s) 35-39
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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), 2024. Published by Science Publishing Group

Keywords

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

References
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Author Information
  • School of Information, Qilu University of Technology, Jinan, China

  • School of Information, Qilu University of Technology, Jinan, China

  • Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China

  • Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China

  • Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China

  • School of Information, Qilu University of Technology, Jinan, China

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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://download.sciencepg.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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