• DocumentCode
    2125022
  • Title

    Fuzzy Expert System Design for Diagnosis of Liver Disorders

  • Author

    Neshat, M. ; Yaghobi, M. ; Naghibi, M.B. ; Esmaelzadeh, A.

  • Author_Institution
    Dept. of Comput. Eng., Azad Univ. of Mashhad Iran, Mashhad
  • fYear
    2008
  • fDate
    21-22 Dec. 2008
  • Firstpage
    252
  • Lastpage
    256
  • Abstract
    In spite of all the standardization methods in medical diagnosis, a correct diagnosis is still considered to be an art .much of this situation is for, that medical diagnosis needs proficiency as well as experience in dealing with uncertainty. Although, in our mechanized age, boundaries of medical science have extremely expanded, you can not overcome this uncertainty easily. Offering a powerful framework to construct the model of existing systems causes fuzzy theory to change to a valuable factor towards medical diagnosis improvement. In this research, a fuzzy system has been designed for learning, analysis and diagnosis of liver disorders. Required data has been chosen from trusty database (UCI) that has 345 records and 6 fields as the entrance parameters and rate of liver disorder risks is used as the system resulting. This system in comparison with other traditional diagnostic systems is faster, cheaper, and also more liable and more accurate. One can uses this system as a specialist assistant or for training medicine students. Also,on time diagnosis of disease and appointing the rate of liver disorders improvement has been experienced and its Verification 91%.
  • Keywords
    diagnostic expert systems; fuzzy set theory; fuzzy systems; learning (artificial intelligence); liver; medical diagnostic computing; fuzzy expert system design; fuzzy learning system; fuzzy subset theory; liver disorder diagnosis; medical diagnosis; Art; Databases; Fuzzy systems; Hybrid intelligent systems; Liver; Medical diagnosis; Medical diagnostic imaging; Power system modeling; Standardization; Uncertainty; expert system; fuzzy; liver; medical;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge Acquisition and Modeling, 2008. KAM '08. International Symposium on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3488-6
  • Type

    conf

  • DOI
    10.1109/KAM.2008.43
  • Filename
    4732824