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

doi:10.1109/ITAB.2003.1222505

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 proceeding › Conference proceeding contribution › 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 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 language	English
Title of host publication	4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	181-183
Number of pages	3
ISBN (Print)	0780376676
DOIs	https://doi.org/10.1109/ITAB.2003.1222505
Publication status	Published - 2003
Externally published	Yes
Event	4th International Conference on Information Technology Applications in Biomedicine (ITAB 2003) - Birmingham, United Kingdom Duration: 24 Apr 2003 → 26 Apr 2003

Publication series

Name	IEEE Engineering in Medicine and Biology Society Annual Conference (EMBS)
Publisher	IEEE

Conference

Conference	4th International Conference on Information Technology Applications in Biomedicine (ITAB 2003)
Country/Territory	United Kingdom
City	Birmingham
Period	24/04/03 → 26/04/03

Keywords

Medical decision-making
soft computing
Multi-Layer
Perceptron neural networks
logistic regression
COLORECTAL-CANCER
DIAGNOSIS
NETWORKS

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10.1109/ITAB.2003.1222505

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 Engineering in Medicine and Biology Society Annual Conference (EMBS)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ITAB.2003.1222505

Ji, Wei ; Naguib, Raouf N. G. ; Macall, John et al. / 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. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2003. pp. 181-183 (IEEE Engineering in Medicine and Biology Society Annual Conference (EMBS)).

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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 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.",

keywords = "Medical decision-making, soft computing, Multi-Layer, Perceptron neural networks, logistic regression, COLORECTAL-CANCER, DIAGNOSIS, NETWORKS",

author = "Wei Ji and Naguib, {Raouf N. G.} and John Macall and Dobrila Petrovic and Elena Gaura and Mohamed Ghoneim",

year = "2003",

doi = "10.1109/ITAB.2003.1222505",

language = "English",

isbn = "0780376676",

series = "IEEE Engineering in Medicine and Biology Society Annual Conference (EMBS)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "181--183",

booktitle = "4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003",

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note = "4th International Conference on Information Technology Applications in Biomedicine (ITAB 2003) ; Conference date: 24-04-2003 Through 26-04-2003",

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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 Engineering in Medicine and Biology Society Annual Conference (EMBS), Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 181-183, 4th International Conference on Information Technology Applications in Biomedicine (ITAB 2003), Birmingham, United Kingdom, 24/04/03. https://doi.org/10.1109/ITAB.2003.1222505

Prognostic prediction of bilharziasis-related bladder cancer by neuro-fuzzy classifier. / Ji, Wei; Naguib, Raouf N. G.; Macall, John et al.
4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2003. p. 181-183 (IEEE Engineering in Medicine and Biology Society Annual Conference (EMBS)).

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

TY - GEN

T1 - Prognostic prediction of bilharziasis-related bladder cancer by neuro-fuzzy classifier

AU - Ji, Wei

AU - Naguib, Raouf N. G.

AU - Macall, John

AU - Petrovic, Dobrila

AU - Gaura, Elena

AU - Ghoneim, Mohamed

PY - 2003

Y1 - 2003

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 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.

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 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.

KW - Medical decision-making

KW - soft computing

KW - Multi-Layer

KW - Perceptron neural networks

KW - logistic regression

KW - COLORECTAL-CANCER

KW - DIAGNOSIS

KW - NETWORKS

U2 - 10.1109/ITAB.2003.1222505

DO - 10.1109/ITAB.2003.1222505

M3 - Conference proceeding contribution

SN - 0780376676

T3 - IEEE Engineering in Medicine and Biology Society Annual Conference (EMBS)

SP - 181

EP - 183

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

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 4th International Conference on Information Technology Applications in Biomedicine (ITAB 2003)

Y2 - 24 April 2003 through 26 April 2003

ER -

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. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2003. p. 181-183. (IEEE Engineering in Medicine and Biology Society Annual Conference (EMBS)). doi: 10.1109/ITAB.2003.1222505

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

Abstract

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Keywords

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