IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/5679837.html
   My bibliography  Save this article

Establishment and Analysis of a Combined Diagnostic Model of Liver Cancer with Random Forest and Artificial Neural Network

Author

Listed:
  • Runzhi Yu
  • Ziyi Cao
  • Yiqin Huang
  • Xuechun Zhang
  • Jie Chen
  • Yuchen Li

Abstract

The incidence of liver cancer (hepatocellular carcinoma; HCC) is rising and with poor clinical outcome expected, a more accurate judgment of tumor tissues and adjacent nontumor tissues is necessary. The aim of this study was to construct a diagnostic model based on random forest (RF) and artificial neural network (ANN). It can be used to aid in the identification of diseased tissue such as cancerous tissue, for HCC clinical diagnosis and surgical guidance. GSE36376 and GSE121248 from Gene Expression Omnibus (GEO) were used as training sets in this investigation. R package “limma†and WGCNA were used to filter the training set for statistically significant p

Suggested Citation

  • Runzhi Yu & Ziyi Cao & Yiqin Huang & Xuechun Zhang & Jie Chen & Yuchen Li, 2022. "Establishment and Analysis of a Combined Diagnostic Model of Liver Cancer with Random Forest and Artificial Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-17, May.
  • Handle: RePEc:hin:jnlmpe:5679837
    DOI: 10.1155/2022/5679837
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/5679837.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/mpe/2022/5679837.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/5679837?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:hin:jnlmpe:5679837. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.