Classification of data related to breast cancer surgery, a comparison between solution by Neural and Fuzzy networks

Authors

DOI:

https://doi.org/10.5965/2764747102042013050

Keywords:

neural network, Fuzzy, breats cancer, datamining

Abstract

This paper presents a comparative study of two techniques for machine-learning concepts with artificial intelligence, using Neural Networks and Fuzzy controllers to classify data from patients undergoing surgery for removal of cancerous breast nodules. Process of data classification can be simple or complex depending on both the problem domain as well as the quality of available data. The accuracy of the results may also vary according to the problem domain, and some cases may permit a greater or lesser margin of error. The objective of this work is to demonstrate both the processes of data classification by a fuzzy approach and by using Neural networks, and also demonstrate how the techniques achieved the best results and with what settings.

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Author Biographies

Ademir Cristiano Gabardo, Federal Technological University of Paraná, UTFPR, Brazil.

PhD in Computing Sciences from the University of Newcastle Australia, NEWCASTLE, Australia

Has a Master’s degree in Technology from the Paraná Federal Technological University, UTFPR, Brazil.

Graduated in Information Systems at the Santa Catarina State University, UDESC, Brazil.

Martín Pérez, Federal Technological University of Paraná, UTFPR, Brazil.

Has a Master’s degree in Applied Computing Sciences from the Paraná Federal Technological University, UTFPR, Brazil.

Graduated in Accounting Sciences at the Paraná Federal University, UFPR, Brazil

References

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MEHROTRA, K., Mohan, C. K. & Ranka, S. (1997). Elements of artificial neural networks. the MIT Press.

MELO, L. G. d. (2011). Sistemas Fuzzy Probabilísticos: Geração Automática De Regras E Defuzzificação Bayesiana. Unpublished master's thesis, UTFPR - Unversidade Tecnologica Federal do Paraná.

WANG, L.-X. & Mendel, J. (1992). Generating fuzzy rules by learning from examples.

IEEE Transactions on Systems, Man and Cybernetics 22, 1414 - 1427. YAO, X. (1999). Evolving Artificial Neural Networks.

ZADEH, L. (1965). Fuzzy Sets. Information and Control.

ZADEH, L. A. (1975). The Concept of a Linguistic Variable and its Application to Approximate Reasoning. Journal of Information Science, 199.

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Published

2013-12-23

How to Cite

Gabardo, A. C., & Pérez, M. (2013). Classification of data related to breast cancer surgery, a comparison between solution by Neural and Fuzzy networks. Revista Brasileira De Contabilidade E Gestão, 2(4), 50–59. https://doi.org/10.5965/2764747102042013050

Issue

Vol. 2 No. 4 (2013)

Section

Articles

License

Copyright (c) 2015 Ademir Cristiano Gabardo, Martín Pérez

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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