• DocumentCode
    395142
  • Title

    Prognostic systems for NPC: a comparison of the multi layer perceptron model and the recurrent model

  • Author

    Abdul-Kareem, Sameem ; Baba, Sapiyan ; Zubairi, Yong Zulina ; Prasad, U. ; Ibrahim, Mohd ; Wahid, A.

  • Author_Institution
    Fac. of Comput. Sci. & Inf. Technol., Malaya Univ., Kuala Lumpur, Malaysia
  • Volume
    1
  • fYear
    2002
  • fDate
    18-22 Nov. 2002
  • Firstpage
    271
  • Abstract
    Artificial neural networks are considered good alternatives to conventional statistical methods for the prediction of survival. Neural networks have been used in medicine since the late 1980s, first, as an aid to diagnosis and treatment and then, recently, as a tool to study medical prognosis of a variety of diseases. Survival predictions at the individual level can help patients make informed decisions with regards to the quality of life and finance. We describe our research in the use of neural network to predict the prognosis of nasopharyngeal carcinoma. Two prognostic models for nasopharyngeal carcinoma were developed, namely the multi-layer perceptron model and the recurrent model and their performance compared.
  • Keywords
    cancer; medical diagnostic computing; multilayer perceptrons; recurrent neural nets; NPC; artificial neural network; back propagation; informed decisions; medical prognosis; medicine; multi layer perceptron model; multi-layer perceptron model; nasopharyngeal carcinoma; patients; prognostic systems; recurrent model; statistical methods; survival analysis; survival prediction; survival predictions; Artificial neural networks; Cancer; Data analysis; Diseases; Information technology; Mathematical model; Medical diagnostic imaging; Neural networks; Predictive models; Statistical analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
  • Print_ISBN
    981-04-7524-1
  • Type

    conf

  • DOI
    10.1109/ICONIP.2002.1202176
  • Filename
    1202176