• DocumentCode
    3274361
  • Title

    Study on classification rules of hypertension based on decision tree

  • Author

    Zhang Wei ; Zhang Xuan ; Chen Junjie

  • Author_Institution
    Inf. Center, Shanxi Med. Coll. for Continuing Educ., Taiyuan, China
  • fYear
    2013
  • fDate
    23-25 May 2013
  • Firstpage
    93
  • Lastpage
    96
  • Abstract
    To extract classification rules of hypertension more effectively, the C4.5 decision tree algorithm was put forward. Firstly, C4.5 algorithm was used to analyse pre-processed hypertension data and a decision tree model was constructed. Then, the understandable classification rules were extracted and the maintaining method was used to test the accuracy of the classification results. Finally, the improved C4.5 algorithm was proposed to amend the information gain of the selected attribute by introducing the concept of correlation. The improved decision tree model more conforms to the medical understanding and the classification accuracy was also improved. The experiment results show that the improved algorithm was effective.
  • Keywords
    data analysis; data mining; decision trees; medical diagnostic computing; pattern classification; C4.5 decision tree algorithm; classification accuracy; correlation concept; hypertension classification rules; hypertension data analysis; information gain; maintaining method; medical understanding; understandable classification rules; Accuracy; Education; Hardware design languages; Hypertension; Standards; Sugar; Terminology; correlation; data mining; decision tree; hypertension;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering and Service Science (ICSESS), 2013 4th IEEE International Conference on
  • Conference_Location
    Beijing
  • ISSN
    2327-0586
  • Print_ISBN
    978-1-4673-4997-0
  • Type

    conf

  • DOI
    10.1109/ICSESS.2013.6615263
  • Filename
    6615263