• DocumentCode
    2544256
  • Title

    EasyMiner: data mining in medical databases

  • Author

    Saraee, Mohammad ; Koundourakis, George ; Theodoulidis, Babis

  • fYear
    1998
  • fDate
    36088
  • Firstpage
    42552
  • Lastpage
    42554
  • Abstract
    Data mining techniques have rarely been applied to medical domain. The University of Manchester Institute of Science and Technology (UMIST) is currently in the process of experimenting with a data mining project using an extensive clinical database of stroke patients from East Lancashire to identify factors that contribute to this disease. EasyMiner is our data mining system designed and developed in the Timelab research laboratory at UMIST for interactive mining of interesting patterns in time-oriented medical databases. This system implements a wide spectrum of data mining functions, including generalisation, relevance analysis, classification and discovery of association rules. The eventual goal of this data mining effort is to identify factors that will improve the quality and cost effectiveness of patient care. In this paper, we briefly describe the EasyMiner data mining approach
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Intelligent Methods in Healthcare and Medical Applications (Digest No. 1998/514), IEE Colloquium on
  • Conference_Location
    York
  • Type

    conf

  • DOI
    10.1049/ic:19981038
  • Filename
    744743