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Generic Medical Fuzzy Expert System for Diagnosis of Cardiac Diseases

by Smita Sushil Sikchi, Sushil Sikchi, Ali M. S.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 13
Year of Publication: 2013
Authors: Smita Sushil Sikchi, Sushil Sikchi, Ali M. S.
10.5120/11147-6234

Smita Sushil Sikchi, Sushil Sikchi, Ali M. S. . Generic Medical Fuzzy Expert System for Diagnosis of Cardiac Diseases. International Journal of Computer Applications. 66, 13 ( March 2013), 35-44. DOI=10.5120/11147-6234

@article{ 10.5120/11147-6234,
author = { Smita Sushil Sikchi, Sushil Sikchi, Ali M. S. },
title = { Generic Medical Fuzzy Expert System for Diagnosis of Cardiac Diseases },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 13 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 35-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number13/11147-6234/ },
doi = { 10.5120/11147-6234 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:22:29.652568+05:30
%A Smita Sushil Sikchi
%A Sushil Sikchi
%A Ali M. S.
%T Generic Medical Fuzzy Expert System for Diagnosis of Cardiac Diseases
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 13
%P 35-44
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The logical thinking of medical practitioners play significant role in decision making about diagnosis and exhibit variation in decisions because of their approaches to deal with uncertainties and vagueness in the knowledge and information. Fuzzy logic has proved to be the remarkable tool for building intelligent decision making systems for approximate reasoning that can appropriately handle both the uncertainty and imprecision. The attempt has been made to explore the capabilities and potentialities of fuzzy expert systems for the emulation of thought in a much more general sense although confined to medical diagnosis. Generic medical fuzzy expert system for diagnosis of cardiac diseases is designed. Mathematical model is developed to predict the risk of heart disease and to compare with the performance of fuzzy expert system. Reported the user friendly decision support system developed for medical practitioners as well as patients. A mathematical model is developed to justify performance of fuzzy expert system.

References
  1. Abraham A. 2005. Rule-based Expert Systems. Handbook of Measuring System Design, John Wiley & Sons, 909-919.
  2. Adlassnig, K. P. , Kolarz, G. , Scheithauer, W. 1985. Present State of the Medical Expert System CADIAG-2. Methods of Information in Medicine, 24: 13-20.
  3. Ahmed, A. , Bigand, A. , Lethuc, V. , Allious, P. M. 2004. Visual Acuity of Vision Tested by Fuzzy Logic: An Application in Ophthalmology as a Step Towards a Telemedicine Project. Information Fusion, 5:217-230.
  4. Ali, Z. , Singh, V. , (2010). Potentials of Fuzzy Logic: An Approach To Handle Imprecise Data. American Medical Informatics Association, 2, 4: 358-361.
  5. Ameri, A. , Moshtaghi H. , Design and Development of an Expert System in Differential Diagnosis of Maxillofacial Radio-lucent Lesions. Web URL, http://www. idt. mdh. se/ kurser/ct3340/archives/ht08/papersRM08/21. pdf.
  6. Aruna P. , Puviarasan N. , Palaniappan B. 2005. An Investigation of Neuro-Fuzzy System in Psychosomatic Disorders. Expert Systems with Applications, 28: 673-679.
  7. Asghar, M. Z. , Khan, A. R. , Asghar, M. J. 2009. Computer Assisted Diagnoses For Red Eye (CADRE). Int. J. of Compouter Science & Engineering, 1, 3: 163-170.
  8. Banerjee, A. , Majumdar, A. K. , Basu, A. 1994. A Fuzzy Expert System Approach Using Multiple Experts for Dynamic Follo-up of Endemic Diseases. Sadhana, India, 19, 1: 51-73.
  9. Berman, L. , Cullen, M. R. , Miller, P. L. 1992. Automated Integration of External Databases : A Knowledge-Based Approach to Enhancing Rule-Based Expert Systems. Proc. Annu Symp Comput Appl Med Care. 227-233.
  10. Binaghi, E. , Gallo, I. , Ghiselli, C. , Levrini, L. , Biondi, K. 2008. An Integrated Fuzzy Logic And Web-Based Framework For Active Protocol Support. International Journal of Medical Informatics, 77: 256-271.
  11. Boegl, K. , Adlassnig, K. P. , Hayashi, Y. , Rothenfluh, T. E. , Leitich, H. 2004. Knowledge Acquisition in the Fuzzy Knowledge Representation Framework of a Medical Consultation System. Artificial Intelligence in Medicine. 30: 1-26.
  12. Chu, W. H. 1988. Generic Expert System Shell For Diagnostic Reasoning. Proc. of 1st Int. Conf. on Industrial and engineering applications of artificial intelligence & expert systems, NY, 1: 7-12.
  13. Ciabattoni, A. , Vetterlein, T. , Adlassnig, K. 2009. A Formal Logical Framework for Cadiag-2. Studies in Health Technology & Informatics, 150: 648-652.
  14. Engle, R. L. , Flehinger, B. J. 1987. Why Expert Systems for Medical Diagnosis are not being generally used: A valedictory opinion. Bulletin of the New York Academy of Medicine, 63, 2: 193-198.
  15. Farshchi, S. M. R. , Yaghoobi, M. 2011. Fuzzy Logic Expert Systems in Hospital : A Foundation View. Journal of Applied Sciences, 1-5.
  16. Isik, H. , Arslan, S. , (2011), "The Design of Ultrasonic Therapy Device via Fuzzy Logic", Expert Systems with Applications, 38, pp 7342-7348.
  17. Jung, I. , Wang, G. 2007. User Pattern Learning Algorithm based MDSS (Medical Decision Support System) Framework under Ubiquitous. World Academy of Science, Engineering and Technology, 36: 184-188.
  18. Kerdprasop, N. , Kerdprasop, K. 2011. Higher Order Programming to Mine Knowledge for a Modern Medical Expert System. Int. J. of Computer Science, 8, 3: 64-72.
  19. Lin, F. , Ying, H. , MacArthur, R. D. , Cohn, J. A. , Jones, D. B. , Crane, L. R. 2007. Decision Making in Fuzzy Discrete Event Systems. Information Sciences, 177:3749-3763.
  20. Masulli, F. , Schenone, A. 1999. A Fuzzy Clustering Based Segmentation System as Support to Diagnosis in Medical Imaging. Artificial Intelligence in Medicine, 16: 129-147.
  21. Muino, D. P. , A Probabilistic Interpretation of the Medical Expert System CADIAG-2. Web URL, http:// www. logic. at/WWTF016/PaperCadiag2Probability. pdf.
  22. Owaied, H. H. , Qasem, M. M. 2010. Developing Rule-Case-Based Shell Expert System. Proc. of Int. Multi Conf. of Engineers and Scientists, 1.
  23. Pan, J. , DeSouza, G. N. , Kak, A. C. 1998. Fuzzy Shell : A Large-Scale Expert System Shell Using Fuzzy Logic for Uncertainty Reasoning. IEEE Transactions on Fuzzy Systems, 6, 4: 563-68.
  24. Pereira, JCR, Tonelli, P. A. , Barros, L. C. , Ortega, N. R. S. 2002. Defuzzification in Medical Diagnosis. Advances in Logic, Artificial Intelligence & Robotics. 202-207.
  25. Qu, Y. , Fu, T. , Qiu, H. 2008. A Fuzzy Expert System Framework Using Object Oriented Techniques. IEEE Pacific Asia Workshop on Computational Intelligence and Industrial Applications, 2: 474-477.
  26. Seising, R. 2004. A History of Medical Diagnosis Using Fuzzy Relations. Fuzziness in Finland'04, 1-5.
  27. Snae, C. , Brueckner, M. 2008. Personal Health Assistance Service Expert System. World Academy of Science, Engineering and Technology : A Biological & Biomedical Sciences, 4, 2: 109-112.
  28. Tomar, P. P. , Saxena, P. K. 2011. Architecture For Medical Diagnosis Using Rule-Based Technique. First Int. Conf. on Interdisciplinary Research & Development, Thailand, 25. 1-25. 5.
  29. Tsipouras, M. G. , Voglis, C. , Fotiadis, D. I. 2007. A Framework for Fuzzy Expert System Creation - Application to Cardiovascular Diseases. IEEE Transactions Biomedical Engg. , 54, 11: 2089-2105.
  30. Vetterlein, T. , Ciabattoni, A. 2010. On the (fuzzy) Logical Content of CADIAG-2. Fuzzy Sets and Systems, 161, 1941-1958.
  31. Wain, R. A. , Tuhrim, S. , D'Autrechy, L. , Reggia, J. A. 1992. The Design and Automated Testing of an Expert System for the Differential Diagnosis of Acute Stroke. American Medical Informatics Association, 94-98.
  32. Zahan, S. 2001. A Fuzzy Approach to Computer-Assisted Myocardial Ischemia Diagnosis. Artificial Intelligence in Medicine, 2, 271-275.
  33. Zarandi M. H. F. , Zolnoori M. , Moin M. , Heidarnejad H. 2010. A Fuzzy Rule Based Expert System for Diagnosing Astham. Industrial Engineering, 17, 2:129-142.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy expert system generic framework medical diagnosis risk predictive model

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