Advertisement

Comparison of Rules Synthesis Methods Accuracy in the System of Type 1 Diabetes Prediction

  • Rafal Deja
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 118)

Abstract

While creating the decision support system we encounter the classification accuracy problem. In the paper author compares the accuracy of two rules synthesis algorithms based on the rough set theory. This comparison is based on the medical support system that goal is to predict the illness among the children with genetic susceptibility to DMT1. The system can help to recommend including a person to pre-diabetes therapy.

Keywords

Decision Support System Decision Table Rule Induction Minimal Complex Healthy Sibling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problems. In: Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. STUDFUZZ, vol. 56, pp. 49–88. Physica-Verlag, Heidelberg (2000)Google Scholar
  2. 2.
    Bazan, J.G., Szczuka, M.S., Wróblewski, J.: A New Version of Rough Set Exploration System. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 397–404. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Deja, G., Jarosz-Chobot, P., Polañska, J., Siekiera, U., Maşecka-Tendera, E.: Is the association between TNF-alpha-308 A allele and DMT1 independent of HLA-DRB1, DQB1 alleles? Mediators Inflamm. 2006, 19724 (2006)CrossRefGoogle Scholar
  4. 4.
    Deja, R.: Applying rough set theory to the system of type 1 diabetes prediction. In: Tkacz, E., Kapczynski, A. (eds.) Internet – Technical Development and Applications. AISC, vol. 64, pp. 119–127. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Grzymala-Busse, J., Wang, A.: Modified algorithms lem1 and lem2 for rule induction from data with missing attribute values. In: Proc. of 5th Int. Workshop on Rough Sets and Soft Computing, pp. 69–72 (1997)Google Scholar
  6. 6.
    Grzymala-Busse, J.W.: Mlem2-discretization during rule induction. In: Proceedings of the International IIS, pp. 499–508 (2003)Google Scholar
  7. 7.
    Grzymala-Busse, J.W.: Selected Algorithms of Machine Learning from Examples. Fundamenta Informaticae 18, 193–207 (1993)zbMATHGoogle Scholar
  8. 8.
    Ilczuk, G., Wakulicz-Deja, A.: Rough sets approach to medical diagnosis system. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS (LNAI), vol. 3528, pp. 204–210. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Komorowski, H.J., Pawlak, Z., Polkowski, L.T., Skowron, A.: Rough Sets: A Tutorial, pp. 3–98. Springer, Singapore (1999)Google Scholar
  10. 10.
    Midelfart, H., Komorowski, H.J., Norsett, K.G., Yadetie, F., Sandvik, A.K., Laegreid, A.: Learning rough set classifiers from gene expressions and clinical data. Fundamenta Informaticae 53, 155–183 (2002)MathSciNetGoogle Scholar
  11. 11.
    Pawlak, Z.: Rough Sets: Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Boston (1991)zbMATHGoogle Scholar
  12. 12.
    Skowron, A., Rauszer, G.: The discernibility matrices and functions in information systems. In: Sşowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–336. Kluwer Academic Publishers (1992)Google Scholar
  13. 13.
    Slowinski, K., Stefanowsk, J., Siwinski, R.: Application of rule induction and rough sets to verification of magnetic resonance diagnosis. Fundam. Inform. 53, 345–363 (2002)Google Scholar
  14. 14.
    Tsumoto, S.: Extracting structure of medical diagnosis: Rough set approach. In Wang, G., Liu, Q., Yao, Y., Skowron, A., eds.: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 78–88. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Tsumoto, S.: Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model. Information Sciences: An International Journal 162, 65–80 (2004)MathSciNetGoogle Scholar
  16. 16.
    Wakulicz-Deja, A., Paszek, P.: Applying rough set theory to multi stage medical diagnosing. Fundamenta Informaticae 54, 387–408 (2003)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceAcademy of Business in Dabrowa GorniczaDabrowa GorniczaPoland

Personalised recommendations