Prognostic prediction of bilharziasis-related bladder cancer by neuro-fuzzy classifier

Wei Ji, R.N.G. Naguib, J. MacAll, D. Petrovic, E. Gaura, M. Ghoneim

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

Cancer prognostic prediction requires a classification system that is robust to the interaction and uncertainty of input factors, as well as being interpretable on the decision made. In this paper, a hybrid neuro-fuzzy classifier is applied to determine the long-term outcome of patients with bilharziasis-related bladder cancer. The same data set is also analysed by a multi-layer perception neural network (MLPNN) and logistic regression, which are both widely used in the area of medical decision-making. In order to better assess the value of this neuro-fuzzy classifier, a benchmark data set used in this area of oncology, the Wisconsin breast cancer data (WBCD), is examined by the above three methods. The study demonstrates that the hybrid neuro-fuzzy classifier is efficient in cancer data analysis and it yields a high classification rate of 97.1% for WBCD, and 84.9% for the bladder cancer data, respectively. © 2003 IEEE.
Original languageEnglish
Title of host publication4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003
PublisherIEEE
Pages181-183
Number of pages3
ISBN (Print)0-7803-7667-6
DOIs
Publication statusPublished - 18 Aug 2003
EventInformation Technology Applications in Biomedicine -
Duration: 24 Apr 200326 Apr 2003

Conference

ConferenceInformation Technology Applications in Biomedicine
Period24/04/0326/04/03

Bibliographical note

This paper was given at the Information Technology Applications in Biomedicine, 2003. 4th International IEEE EMBS Special Topic. This paper is not available on the repository

Keywords

  • Decision making
  • Diseases
  • Fuzzy inference
  • Fuzzy sets
  • Fuzzy systems
  • Network layers
  • Neural networks
  • Regression analysis
  • Soft computing, Breast cancer data
  • Classification rates
  • Classification system
  • Logistic regressions
  • Medical decision making
  • Multi-layer perceptron neural networks
  • Multilayer perception neural networks
  • Neuro fuzzy classifier, Classification (of information)

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  • Cite this

    Ji, W., Naguib, R. N. G., MacAll, J., Petrovic, D., Gaura, E., & Ghoneim, M. (2003). Prognostic prediction of bilharziasis-related bladder cancer by neuro-fuzzy classifier. In 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003 (pp. 181-183). IEEE. https://doi.org/10.1109/ITAB.2003.1222505