• DocumentCode
    2955724
  • Title

    Discovery of association rules in Metabolic Syndrome related diseases

  • Author

    Chan, Chien-Lung ; Chen, Chien-Wei ; Liu, Baw-Jhiune

  • Author_Institution
    Dept. of Inf. Manage., Yuan Ze Univ., Chungli
  • fYear
    2008
  • fDate
    1-8 June 2008
  • Firstpage
    856
  • Lastpage
    862
  • Abstract
    Since 1980, the hypertension and diabetes mellitus in metabolic syndrome have appeared in the top ten causes of death every year in Taiwan. This research aims to study metabolic syndrome related disease by using data mining technique, and to understand the strength of association between diabetes mellitus, hypertension and hyperlipidemia. The data of this research came from the National Health Insurance Research Database provided by the Bureau of National Health Insurance, Department of Health. It includes the diabetes mellitus patientspsilapsila health insurance record during 2003-2005 in Taiwan. We used association rules to find diseases patterns of metabolic syndrome related disease. Using data mining technique can find and confirm the relation between diseases. We found diabetes mellitus is related to oral diseases and blear eyes. We also found that patients with metabolic syndrome have higher connection with liver diseases than patients with diabetes mellitus.
  • Keywords
    data mining; diseases; medical information systems; National Health Insurance Research Database; association rules; data mining technique; diabetes mellitus; hyperlipidemia; metabolic syndrome related diseases; oral diseases; patients health insurance record; Association rules; Blood; Cardiac disease; Cardiovascular diseases; Data mining; Databases; Diabetes; Hypertension; Insurance; Liver diseases; Association Rules; Metabolic Syndrome;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1820-6
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2008.4633898
  • Filename
    4633898