Skip to main content
SpringerLink
Log in
Book cover

North American Fuzzy Information Processing Society Annual Conference

NAFIPS 2018: Fuzzy Information Processing pp 24–36Cite as

Design and Implementation of Fuzzy Expert System Based on Evolutionary Algorithms for Diagnosing the Intensity Rate of Hepatitis C

  • Conference paper
  • First Online:

  • 797 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 831))

Abstract

Hepatitis, is one of the most common and dangerous diseases which affects liver. If hepatitis does not detect early, some side effects such as cirrhosis, hepatocellular carcinoma, liver failure and mature death will be occurred. Among different types of this disease, hepatitis C arises from HCV viruses, is the leading cause of liver disease. Although hepatitis C can be easily diagnosed by a simple test, the intensity rate of this disease is a qualitative and controversial issue. This paper attempts to design a fuzzy expert system for diagnosing the intensity rate of hepatitis C with FibroScan results. The proposed system includes three steps: pre-processing, create the primary fuzzy system and optimize the membership functions’ parameters. KNN method is used for filling missing data; moreover, feature selection is done by decision tree and genetic algorithm. The primary fuzzy system is established and in the third step, three different evolutionary algorithms are implemented to optimize the parameters of primary system. Results portray that Differential Evolution algorithm presents better performance in learning the pattern of data and decreases the error around 30%.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Mean Squared Error.

  2. 2.

    Root Mean Square Error.

  3. 3.

    Adaptive Neuro Fuzzy Inference System.

References

  1. Modi, A.A., Liang, T.J.: Hepatitis C: a clinical review. Oral Dis. 14(1), 10–14 (2008)

    Article  Google Scholar 

  2. Patterson, D.: Introduction to Artificial Intelligence and Expert Systems. Prentice-Hall Inc., Upper Saddle River (1990)

    Google Scholar 

  3. Hasan, M.A., Chowdhury, A.R.: Human disease diagnosis using a fuzzy expert system. arXiv preprint arXiv:1006.4544 (2010)

  4. Neshat, M., Yaghobi, M.: Designing a fuzzy expert system of diagnosing the hepatitis B intensity rate and comparing it with adaptive neural network fuzzy system. In: Proceedings of the World Congress on Engineering and Computer Science, vol. 2, pp. 797–802, October 2009

    Google Scholar 

  5. Sotudian, S., Zarandi, M.F., Turksen, I.B.: From Type-I to Type-II fuzzy system modeling for diagnosis of hepatitis. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 10(7), 1280–1288 (2016)

    Google Scholar 

  6. Duch, W., Grudzi, N.K., Diercksen, G.: Neural minimal distance methods. In: Proceedings of the 3-rd Conference on Neural Networks and Their Applications, Kule, Poland, pp. 183–188, October 1997

    Google Scholar 

  7. Duch, W., Grudziński, K.: Ensembles of similarity-based models. In: Kłopotek, M.A., Michalewicz, M., Wierzchoń, S.T. (eds.) Intelligent Information Systems 2001, pp. 75–85. Physica, Heidelberg (2001)

    Google Scholar 

  8. Duch, W., Adamczak, R.: Statistical methods for construction of neural networks. In: ICONIP, pp. 639–642 (1998)

    Google Scholar 

  9. Duch, W., Adamczak, R., Diercksen, G.H.: Neural networks from similarity based perspective. In: Mohammadian, M. (ed.) New Frontiers in Computational Intelligence and its Applications, pp. 93–108. IOS Press, Amsterdam (2000)

    Google Scholar 

  10. Ozyilmaz, L., Yildirim, T.: Artificial neural networks for diagnosis of hepatitis disease. In: Proceedings of the International Joint Conference on Neural Networks, 2003, vol. 1, pp. 586–589. IEEE, July 2003

    Google Scholar 

  11. Vural, R.A., Özyılmaz, L., Yıldırım, T.: A Comparative Study on Computerised Diagnostic Performance of Hepatitis Disease Using ANNs. In: Huang, D.S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS, vol. 4114, pp. 1177–1182. Springer, Heidelberg (2006). https://doi.org/10.1007/978-3-540-37275-2_145

    Chapter  Google Scholar 

  12. Polat, K., Güneş, S.: Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation. Digit. Signal Proc. 16(6), 889–901 (2006)

    Article  Google Scholar 

  13. Šter, B., Dobnikar, A.: Neural networks in medical diagnosis: comparison with other methods. In: International Conference on Engineering Applications of Neural Networks, pp. 427–430 (1996)

    Google Scholar 

  14. Jankowski, N.: Approximation and classification in medicine with IncNet neural networks. In: Machine Learning and Applications. Workshop on Machine Learning in Medical Applications, pp. 53–58, July 1999

    Google Scholar 

  15. Gong, G., Cestnik, B.: UCI Repository of Machine Learning Databases, November 1988. https://archive.ics.uci.edu/ml/machine-learning-databases/hepatitis/

  16. Merat, S.: Interviewee, Shariati Hospital, Hepatitis C Records. [Interview], September 2017

    Google Scholar 

  17. Yim, C.: FibroScan and Liver Disease. University of Health Network, Toronto (2016)

    Google Scholar 

  18. Department of Computer Science, Princeton University. http://www.cs.princeton.edu/courses

  19. Fang, Z., Na, L., Jinhui, L.: Application research of the genetic algorithm on the intelligent test paper composition of examination database. In: Zhang, J. (ed.) ICAIC 2011. CCIS, vol. 227, pp. 443–448. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23226-8_58

    Chapter  Google Scholar 

  20. Zarandi, M.F., Faraji, M.R., Karbasian, M.: An exponential cluster validity index for fuzzy clustering with crisp and fuzzy data. Sci. Iranica. Trans. E Ind. Eng. 17(2), 95 (2010)

    Google Scholar 

  21. Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  22. Escalante, H.J., Montes, M., Sucar, L.E.: Particle swarm model selection. J. Mach. Learn. Res. 10(Feb), 405–440 (2009)

    Google Scholar 

  23. Huang, Z., Chen, Y.: An improved differential evolution algorithm based on adaptive parameter. J. Control Sci. Eng. 2013, 3 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

    Mehrnaz Behrooz & Mohammad Hossein Fazel Zarandi

Authors
  1. Mehrnaz Behrooz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Hossein Fazel Zarandi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehrnaz Behrooz .

Editor information

Editors and Affiliations

  1. Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil

    Guilherme A. Barreto

  2. Department of Statistics & Applied Mathematics, Federal University of Ceará, Fortaleza, Ceará, Brazil

    Ricardo Coelho

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Behrooz, M., Zarandi, M.H.F. (2018). Design and Implementation of Fuzzy Expert System Based on Evolutionary Algorithms for Diagnosing the Intensity Rate of Hepatitis C. In: Barreto, G., Coelho, R. (eds) Fuzzy Information Processing. NAFIPS 2018. Communications in Computer and Information Science, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-319-95312-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95312-0_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95311-3

  • Online ISBN: 978-3-319-95312-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation