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A Fuzzy Logic-Based Decision Support System for the Diagnosis of Arthritis Pain for Rheumatic Fever Patients

  • Sanjib Raj Pandey
  • Jixin Ma
  • Choi-Hong Lai
  • Chiyaba Njovu
Conference paper

Abstract

This paper describes our conceptual ideas and future development framework of Decision support System to diagnose of arthritis pain and determine whether the pain is associated with rheumatic fever or not. Also, it would be used for diagnosing arthritis pain (only for rheumatic fever patients), in four different stages, namely: Fairly Mild, Mild, Moderate and Severe. Our diagnostic tool will allow doctors to register symptoms describing arthritis pain using numerical values that are estimates of the severity of pain that a patient feels. These values are used as input parameters to the system, which invokes rules to determine a value of severity for the arthritis pain.

Keywords

Fuzzy Logic Decision Support System Fuzzy Rule Rheumatic Fever Rheumatic Heart Disease 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Michael Edwards, PhD, MPH, Theo Lippeveld, MD, “Decision Support Systems for Improving the Analytic Capacity of Decentralized Routine Health Information Systems in Developing Countries” http://csdl2.computer.org/comp/proceedings/hicss/2004/2056/06/205660152b.pdf
  2. 2.
    Jonathan R Carapetis and Liesl J Zühlke, “Global research priorities in rheumatic fever and rheumatic heart disease”, Ann Pediatr Cardiol. 2011 Jan-Jun; 4(1): 4–12. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3104531/?tool=pubmed
  3. 3.
    Carmen De Maio, Vincenzo Loia, Giuseppe Fenza, Mariacristina, Roberto Linciano, Aldo Morrone, “Fuzzy Knowledge Approach to Automatic Disease Diagnosis”, 2011 IEEE International Conference on Fuzzy Systems, June 27–30, 2011, Taipei, Taiwan.Google Scholar
  4. 4.
    Hepu Deng and SantosoWibowo, “A Rule-Based Decision Support System for Evaluating and Selecting IS Projects”, Proceedings of the International Multi Conference of Engineers and Computer Scientists 2008 Vol II, IMECS 2008, 19–21 March, 2008, Hong Kong.Google Scholar
  5. 5.
    Obi J.C., Imainvan A.A “Decision Support System for the Intelligent Identification of Alzheimer using Neuro Fuzzy Logic”, International Journal on, Soft Computing, 2(2), May 2011.Google Scholar
  6. 6.
    X.Y. Djam1,*, G. M. Wajiga2, Y. H. Kimbi3 and N.V. Blamah4 “A Fuzzy Expert System for the Management of Malaria” Int. J. Pure Appl. Sci. Technol., 5(2) (2011), pp. 84–108, International Journal of Pure and Applied Sciences and Technology, ISSN 2229–6107.Google Scholar
  7. 7.
    William Siler and James Buckley, “Fuzzy Expert System and Fuzzy Reasoning” Wiley & Sons, Inc pp, 49–50 2005.Google Scholar
  8. 8.
    Stephen Yurkovich, Kevin M. Passino, Department of Electrical Engineering, The Ohio State University, “Fuzzy Control”, Copyright 1998, Addison Wesley Longman, Inc. ISBN 0-201-18074-X.Google Scholar
  9. 9.
    Stephen Yurkovich, Kevin M. Passino, Department of Electrical Engineering, The Ohio State University, “Fuzzy Control”, Copyright 1998, Addison Wesley Longman, Inc. ISBN 0-201-18074-X.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Sanjib Raj Pandey
    • 1
  • Jixin Ma
    • 1
  • Choi-Hong Lai
    • 1
  • Chiyaba Njovu
    • 1
  1. 1.School of Computing & Mathematical ScienceUniversity of GreenwichPark RowLondon

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