• DocumentCode
    3398835
  • Title

    The data mining of TCM syndrome diagnostic criteria by the R_Apriori algorithm

  • Author

    Yong-sen Jiang ; Chu-xiang Chen

  • Author_Institution
    Sci. Res. Office, Beihua Univ., Jilin, China
  • fYear
    2011
  • fDate
    19-22 Aug. 2011
  • Firstpage
    2377
  • Lastpage
    2380
  • Abstract
    As the association rules mining algorithm, Apriori algorithm is gotten a lot of application used for its easy use. However, it often encountered some problem as low mining efficiency, too many invalid rules acquired and the rules of pattern mining disorder. In this paper, an algorithm called R_Apriori which improved from Apriori algorithm is designed for above problems. It is necessary to build syndrome diagnosis criteria of Traditional Chinese Medical for solution it´s non-scientificalness of experience medical. With treatment on the elderly virus pneumonia, a series of process including initial null handle, subtraction operation, reducing data dimension. By data mining based on R_Apriori algorithm, syndrome diagnostic criteria of bacterial pneumonia in the elderly were defined. The method of establishment TCM diagnosis criteria has worthy of promotion.
  • Keywords
    data mining; medical diagnostic computing; R_Apriori algorithm; TCM syndrome diagnostic criteria; association rules mining algorithm; bacterial pneumonia; data dimension reduction; data mining; elderly virus pneumonia; initial null handle; pattern mining disorder; subtraction operation; traditional chinese medical; Algorithm design and analysis; Association rules; Diseases; Educational institutions; Lungs; Medical diagnostic imaging; Data Mining; R_Apriori Algorithm; Syndrome Diagnosis Criteria; TCM;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on
  • Conference_Location
    Jilin
  • Print_ISBN
    978-1-61284-719-1
  • Type

    conf

  • DOI
    10.1109/MEC.2011.6025971
  • Filename
    6025971