Advertisement

A Classification Method Based on Similarity Measures of Generalized Fuzzy Numbers in Building Expert System for Postoperative Patients

  • Pasi Luukka
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 680)

Abstract

In this research, we concentrate to build an expert system for a problem where task is to determine where patients in a postoperative recovery area should be sent to next. Data set created from postoperative patients is used to build proposed expert system to determine, based on hypothermia condition, whether patients in a postoperative recovery area should be sent to Intensive Care Unit (ICU), general hospital floor, or go home. What makes this task particularly difficult is that most of the measured attributes have linguistic values (e.g., stable, moderately stable, unstable, etc.). We are using generalized fuzzy numbers to model the data and introduce new fuzzy similarity based classification procedure which can deal with these linguistic attributes and classify them accordingly. Results are compared to existing result in literature, and this system provides mean classification accuracy of 66.2% which compared well to earlier results reported in literature.

Keywords

Classification methods Expert systems Generalized fuzzy numbers ICU Postoperative patients Similarity measures 

References

  1. 1.
    Auephanwiriyakul, S., Theera-Umpon, N. (2004). Comparison of linguistic and regular hard C-means in postoperative patient data. Journal of Advanced Computational Intelligence and Intelligent Informatics 8(6), pp. 599–605.Google Scholar
  2. 2.
    Bandemer, H., Näther, W. (1992). Fuzzy data analysis, Kluwer Academic Publisher, Dordrecht.CrossRefGoogle Scholar
  3. 3.
    Chen, S.H. (1999). Ranking generalized fuzzy number with graded mean integration. In Proceedings of the eighth international fuzzy systems association world congress, Vol. 2, pp. 899–902. Taipei, Taiwan, Republic of China.Google Scholar
  4. 4.
    Denoeux, T., Masson, MH. (2004). Principal component analysis of fuzzy data using autoassociative neural networks. IEEE Transactions on Fuzzy Systems 12(3), pp. 336–349.CrossRefGoogle Scholar
  5. 5.
    Luukka, P. (2009), PCA for fuzzy data and similarity classifier in building recognition system for post-operative patient data. Expert Systems with Applications 36, pp. 1222–1228.CrossRefGoogle Scholar
  6. 6.
    Luukka, P., Leppälampi, T. (2006). Similarity classifier with generalized mean applied to medical data. Computers in Biology and Medicine 36, pp. 1026–1040.PubMedCrossRefGoogle Scholar
  7. 7.
    Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
  8. 8.
    Wei, S.H., Chen, S.M. (2009). A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. Expert Systems with Applications 36, pp. 589–598.CrossRefGoogle Scholar
  9. 9.
    Woolery, L., Grzymala-Busse, J., Summers, S., Budihardjo, A. (1991). The use of machine learning program LERSLB 2.5 in knowledge acquisition for expert system development in nursing. Computers in Nursing 9, pp. 227–234.PubMedGoogle Scholar
  10. 10.
    Zadeh, L. (1965). Fuzzy sets. Information and Control 8, pp. 338–353.CrossRefGoogle Scholar
  11. 11.
    Zadeh, L. (1971). Similarity relations and fuzzy orderings. Information Science 3, pp. 177–200.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Laboratory of Applied MathematicsLappeenranta University of TechnologyLappeenrantaFinland

Personalised recommendations