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

Wei Ji, Raouf N. G. Naguib, John Macall, Dobrila Petrovic, Elena Gaura, Mohamed Ghoneim

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

4 Citations (Scopus)


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 Percepton 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.

Original languageEnglish
Title of host publication4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages3
ISBN (Print)0780376676
Publication statusPublished - 2003
Externally publishedYes
Event4th International Conference on Information Technology Applications in Biomedicine (ITAB 2003) - Birmingham, United Kingdom
Duration: 24 Apr 200326 Apr 2003

Publication series

NameIEEE Engineering in Medicine and Biology Society Annual Conference (EMBS)


Conference4th International Conference on Information Technology Applications in Biomedicine (ITAB 2003)
CountryUnited Kingdom


  • Medical decision-making
  • soft computing
  • Multi-Layer
  • Perceptron neural networks
  • logistic regression

Cite this