IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0099422.html
   My bibliography  Save this article

Artificial Neural Network Accurately Predicts Hepatitis B Surface Antigen Seroclearance

Author

Listed:
  • Ming-Hua Zheng
  • Wai-Kay Seto
  • Ke-Qing Shi
  • Danny Ka-Ho Wong
  • James Fung
  • Ivan Fan-Ngai Hung
  • Daniel Yee-Tak Fong
  • John Chi-Hang Yuen
  • Teresa Tong
  • Ching-Lung Lai
  • Man-Fung Yuen

Abstract

Background & Aims: Hepatitis B surface antigen (HBsAg) seroclearance and seroconversion are regarded as favorable outcomes of chronic hepatitis B (CHB). This study aimed to develop artificial neural networks (ANNs) that could accurately predict HBsAg seroclearance or seroconversion on the basis of available serum variables. Methods: Data from 203 untreated, HBeAg-negative CHB patients with spontaneous HBsAg seroclearance (63 with HBsAg seroconversion), and 203 age- and sex-matched HBeAg-negative controls were analyzed. ANNs and logistic regression models (LRMs) were built and tested according to HBsAg seroclearance and seroconversion. Predictive accuracy was assessed with area under the receiver operating characteristic curve (AUROC). Results: Serum quantitative HBsAg (qHBsAg) and HBV DNA levels, qHBsAg and HBV DNA reduction were related to HBsAg seroclearance (P

Suggested Citation

  • Ming-Hua Zheng & Wai-Kay Seto & Ke-Qing Shi & Danny Ka-Ho Wong & James Fung & Ivan Fan-Ngai Hung & Daniel Yee-Tak Fong & John Chi-Hang Yuen & Teresa Tong & Ching-Lung Lai & Man-Fung Yuen, 2014. "Artificial Neural Network Accurately Predicts Hepatitis B Surface Antigen Seroclearance," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-11, June.
    • Handle: RePEc:plo:pone00:0099422
    DOI: 10.1371/journal.pone.0099422
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0099422
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0099422&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0099422?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0099422. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.