Skip to main content

Medical Decision Making through Fuzzy Computational Intelligent Approaches

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5722))

Abstract

A new approach for the construction of Fuzzy Cognitive Maps augmented by knowledge through fuzzy rule-extraction methods for medical decision making is investigated. This new approach develops an augmented Fuzzy Cognitive Mapping based Decision Support System combining knowledge from experts and knowledge from data in the form of fuzzy rules generated from rule-based knowledge discovery methods. Fuzzy Cognitive Mapping (FCM) is a fuzzy modeling methodology based on exploiting knowledge and experience from experts. The FCM accompanied with knowledge extraction and computational intelligent techniques, contribute to the development of a decision support system in medical informatics. The proposed approach is implemented in a well-known medical problem for assessment of treatment planning decision process in radiotherapy.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Dhar, V., Stein, R.: Intelligent Decision Support Methods: The Science of Knowledge Work. Prentice-Hall, Upper Saddle River (1997)

    Google Scholar 

  2. Zurada, J.M., Duch, W., Setiono, R.: Computational intelligence methods for rule-based data understanding. In: Proc. of the IEEE International Conference on Neural Networks, vol. 92(5), pp. 771–805 (2004)

    Google Scholar 

  3. Mitra, S., Hayashi, Y.: Neuro-Fuzzy rule generation: Survey in soft computing. IEEE Trans. Neural Networks 11(3), 748–760 (2000)

    Article  Google Scholar 

  4. Nauck, U.: Design and implementation of a neuro-fuzzy data analysis tool in Java, Master’s thesis, Technical, University of Braunschweig, Braunschweig (1999)

    Google Scholar 

  5. Stylios, C.D., Georgopoulos, V.C., Malandraki, G.A.: Fuzzy cognitive map architectures for medical decision support systems. Appl. Soft Comput. 8(3), 1243–1251 (2008)

    Article  Google Scholar 

  6. Papageorgiou, E.I., Spyridonos, P., Ravazoula, P., Stylios, C.D., Groumpos, P.P., Nikiforidis, G.: Advanced Soft Computing Diagnosis Method for Tumor Grading. Artif. Intell. Med. 36, 59–70 (2006a)

    Article  MATH  Google Scholar 

  7. Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: A Combined Fuzzy Cognitive Map and Decision Trees Model for Medical Decision Making. In: Proceedings of the 28th IEEE EMBS Annual Intern. Conference in Medicine and Biology Society, New York, August 30- September 3, pp. 6117–6120 (2006b)

    Google Scholar 

  8. Papageorgiou, E.I., Stylios, C.D., Groumpos, P.: An Integrated Two-Level Hierarchical Decision Making System based on Fuzzy Cognitive Maps (FCMs). IEEE Trans. Biomed. Engin. 50(12), 1326–1339 (2003)

    Article  Google Scholar 

  9. Kosko, B.: Fuzzy Cognitive Maps. International Journal of Man-Machine Studies 24, 65–75 (1986)

    Article  MATH  Google Scholar 

  10. Lee, K.C., Lee, W.J., Kwon, O.B., Han, J.H., Yu, P.I.: Strategic Planning Simulation Based on Fuzzy Cognitive Map Knowledge and Differential Game. Simulation 75(5), 316–327 (1998)

    MATH  Google Scholar 

  11. Bueno, S., Salmeron, J.L.: Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications 36(3), 5221–5229 (2009)

    Article  Google Scholar 

  12. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Menlo Park (1996)

    Google Scholar 

  13. Kurgan, L.A., Musilek, P.: A Survey on Knowledge Discovery and Data mining processes. The Knowledge Engineering Review 21(1), 1–24 (2006)

    Article  Google Scholar 

  14. Janikow, C.Z.: Fuzzy Decision Trees Manual, free version for Fuzzy Decision Trees (1998), http://www.cs.umsl.edu/Faculty/janikow/janikow.html

  15. Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Trans. Systems Man Cybernet. Part B (Cybernetics) 28(1), 1–14 (1998)

    Article  Google Scholar 

  16. AAPM Report No. 55, American Association of Physicists in Medicine, Report of Task Group 23 of the Radiation Therapy Committee, Radiation Treatment planning dosimetry verification. American Institution of Physics, Woodbury (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Papageorgiou, E.I. (2009). Medical Decision Making through Fuzzy Computational Intelligent Approaches. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04125-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04124-2

  • Online ISBN: 978-3-642-04125-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics