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

doi:10.1109/ITAB.2003.1222505

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

College of Engineering, Environment & Science

Robert Gordon University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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 language	English
Title of host publication	4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003
Publisher	IEEE
Pages	181-183
Number of pages	3
ISBN (Print)	0-7803-7667-6
DOIs	https://doi.org/10.1109/ITAB.2003.1222505
Publication status	Published - 18 Aug 2003
Event	4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine - Birmingham, United Kingdom Duration: 24 Apr 2003 → 26 Apr 2003 Conference number: 4th

Conference

Conference	4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine
Abbreviated title	ITAB
Country/Territory	United Kingdom
City	Birmingham
Period	24/04/03 → 26/04/03

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)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ITAB.2003.1222505

Cite this

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, 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, Birmingham, United Kingdom, 24/04/03. https://doi.org/10.1109/ITAB.2003.1222505

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title = "Prognostic prediction of bilharziasis-related bladder cancer by neuro-fuzzy classifier",

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. {\textcopyright} 2003 IEEE.",

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

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; 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, ITAB ; Conference date: 24-04-2003 Through 26-04-2003",

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AU - Ji, Wei

AU - Naguib, R.N.G.

AU - MacAll, J.

AU - Petrovic, D.

AU - Gaura, E.

AU - Ghoneim, M.

N1 - Conference code: 4th

PY - 2003/8/18

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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.

KW - Decision making

KW - Diseases

KW - Fuzzy inference

KW - Fuzzy sets

KW - Fuzzy systems

KW - Network layers

KW - Neural networks

KW - Regression analysis

KW - Soft computing, Breast cancer data

KW - Classification rates

KW - Classification system

KW - Logistic regressions

KW - Medical decision making

KW - Multi-layer perceptron neural networks

KW - Multilayer perception neural networks

KW - Neuro fuzzy classifier, Classification (of information)

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DO - 10.1109/ITAB.2003.1222505

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SP - 181

EP - 183

BT - 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003

PB - IEEE

T2 - 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine

Y2 - 24 April 2003 through 26 April 2003

ER -

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

Abstract

Conference

Bibliographical note

Keywords

UN SDGs

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