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

Fingerprint

Classifiers
Oncology
Logistics
Decision making
Neural networks
Uncertainty

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)

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

Prognostic prediction of bilharziasis-related bladder cancer by neuro-fuzzy classifier. / Ji, Wei; Naguib, R.N.G.; MacAll, J.; Petrovic, D.; Gaura, E.; Ghoneim, M.

4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003. IEEE, 2003. p. 181-183.

Research output: Chapter in Book/Report/Conference proceedingChapter

Ji, W, Naguib, RNG, 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. IEEE, pp. 181-183, Information Technology Applications in Biomedicine, 24/04/03. https://doi.org/10.1109/ITAB.2003.1222505
Ji W, Naguib RNG, MacAll J, Petrovic D, Gaura E, Ghoneim M. 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. IEEE. 2003. p. 181-183 https://doi.org/10.1109/ITAB.2003.1222505
Ji, Wei ; Naguib, R.N.G. ; MacAll, J. ; Petrovic, D. ; Gaura, E. ; Ghoneim, M. / Prognostic prediction of bilharziasis-related bladder cancer by neuro-fuzzy classifier. 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003. IEEE, 2003. pp. 181-183
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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. {\circledC} 2003 IEEE.",
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N2 - 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.

AB - 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.

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