Study of Diabetes Mellitus (DM) with Ophthalmic Complication Using Association Rules of Data Mining Technique

  • Pornnapas Kasemthaweesab
  • Werasak Kurutach
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6922)


Association Rule Discovery is a significant data mining technique. In this paper, we applied this technique to discover fundamental association among a data set of diabetes mellitus (DM) patients with ophthalmic complication using a classifier based on gender, age and payment method of treatment expense. The result indicated that “diabetes mellitus (DM) patients Type II aging between 60-69 years old with no occupation whose payment for their treatment expense was by Government Official Rights of Continuous Treatment tended to have diabetes mellitus (DM) with ophthalmic complication.” This conclusion is useful for healthcare treatment of adulthood patients, welfare improvement of public healthcare, provision of helpful recommendation for diabetes mellitus patients and further development in finding disease complication.


Data mining Association Rule Diabetes Mellitus 


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  1. 1.
    Chen, H., et al.: Medical Informatics: Knowledge discovery and data mining in medical informatics. Springer, New York (2005)CrossRefGoogle Scholar
  2. 2.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press (1996)Google Scholar
  3. 3.
    Ng, R.T., Pei, J.: Special Issue: Data Mining for Health Informatics. ACMSIGKDD ExplorationGoogle Scholar
  4. 4.
    Chao-ton, S., Chien-hsin, Y., Kuang-hung, H., Wen-ko, C.: Data Mining for the diagnosis of type II diabetes from three-dimensional body surface anthropometrical scanning data. Computer & Mathematics with Applications 51, 1075–1092 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Han, J., Rodriguze, J.C., Beheshti, M.: Diabetes Data Analysis and Prediction Model Discovery Using RapidMiner. In: 2008 Second International Conference on Future Generation Communication and Networking, pp. 69–99 (2008)Google Scholar
  6. 6.
    Zorman, M., Masud, G., Kokol, P., Yamamoto, R., Stiglic, B.: Mining Diabetes Database With Decision Trees and Association Rules. In: Proceedings of the15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002), pp. 134–139 (2002)Google Scholar
  7. 7.
    Patil, B.M., Joshi, R.C., Toshniwal, D.: Association rule for classification of type-2 diabetic patients. In: 2010 Second International Conference on Machine Learning and Computing, pp. 330–334 (2010)Google Scholar
  8. 8.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: , Data Mining to Knowledge Discovery in Databasess. American Association for Artificial Intellingece (1996)Google Scholar
  9. 9.
    Vision Problems in U.S.A. Statistical Analysis. National Society to Prevent Blindness, New York (1980)Google Scholar
  10. 10.
    Diabetic Retinopathy Study Research Group. Design, methods and baseline results. DRS report Number6. Invest Ophthalmol. 21,149–209 (1981)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pornnapas Kasemthaweesab
    • 1
  • Werasak Kurutach
    • 1
  1. 1.Faculty of Information Science and TechnologyMahanakorn University of TechnologyBangkokThailand

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