Prognostic value of histology and lymph node status in bilharziasis-bladder cancer: Outcome prediction using neural networks

Wei Ji, Raouf N. G. Naguib, Dobrila Petrovic, Elena Gaura, M. A. Ghoneim

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

In this paper, the evaluation of two features in predicting the outcomes of patients with bilharziasis bladder cancer has been investigated using an RBF neural network. Prior to prediction, the feature subsets were extracted from the whole set of features for the purpose of providing a high performance of the network. Throughout the analysis of the prognostic feature combinations, two features, histological type and lymph node status, have been identified as the important indicators for outcome prediction of this type of cancer. The highest predictive accuracy reached 85.0% in this study.

Original languageEnglish
Title of host publicationProceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vols 1-4
Place of PublicationNew Jersey, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3870-3873
Number of pages4
ISBN (Print)0780372115
DOIs
Publication statusPublished - 2001
Externally publishedYes
Event23rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society - ISTANBUL, Turkey
Duration: 25 Oct 200128 Oct 2001

Publication series

NameProceedings of Annual International Conference of the IEEE Engineering In Medicine and Biology Society
PublisherIEEE
Volume23
ISSN (Print)1094-687X

Conference

Conference23rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
CountryTurkey
CityISTANBUL
Period25/10/0128/10/01

Keywords

  • Schistosomiasis
  • feature extraction
  • classification
  • survival analysis
  • epidemiology
  • SURVIVAL
  • STAGE

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