IDEAS home Printed from https://ideas.repec.org/a/igg/jehmc0/v10y2019i2p63-85.html
   My bibliography  Save this article

Healthcare: Prediction of Breast Cancer Stage Using Social Spider-Inspired Optimization Algorithm

Author

Listed:
  • Ramani Selvanambi

    (VIT University, Vellore, India)

  • Jaisankar N.

    (VIT University, Vellore, India)

Abstract

Quality analysis of the treatment of cancer has been an objective of e-health services for quite some time. The objective is to predict the stage of breast cancer by using diverse input parameters. Breast cancer is one of the main causes of death in women when compared to other tumors. The classification of breast cancer information can be profitable to anticipate diseases or track the hereditary of tumors. For classification, an artificial neural network (ANN) structure was carried out. In the structure, nine training algorithms are used and the proposed is the Levenberg-Marquardt algorithm. For optimizing the hidden layer and neuron, three optimization techniques are used. In the result, the best approval execution is anticipated and the diverse execution evaluation estimation for three optimization algorithms is researched. The correlation execution diagram for an accuracy of 95%, a sensitivity of 98%, and a specificity of 89% of a social spider optimization (SSO) algorithm are shown.

Suggested Citation

  • Ramani Selvanambi & Jaisankar N., 2019. "Healthcare: Prediction of Breast Cancer Stage Using Social Spider-Inspired Optimization Algorithm," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 10(2), pages 63-85, April.
    • Handle: RePEc:igg:jehmc0:v:10:y:2019:i:2:p:63-85
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJEHMC.2019040104
    Download Restriction: no
    ---><---

    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:igg:jehmc0:v:10:y:2019:i:2:p:63-85. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.