The Recognition of Migraine Headache by Designing Fuzzy Expert System and Using Learning from Examples (LFE) Algorithm

Document Type : Original Article (s)

Authors

1 PhD Candidate of Applied Mathematics, Department of Mathematics, School of Mathematics, Tehran Payame Noor University, Tehran, Iran

2 Associate Professor, Department of Biomedical Engineering, School of Engineering, University of Isfahan, Isfahan, Iran

3 Associate Professor, Department of Neurology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Background: The migraine headache is a kind of most populated headache which its prevalence rate is so high. The first step for starting the treatment is the recognition stage. In addition, the fuzzy logic has good power for describing enigmatic and imprecise aspects; so, this tool could be used for the system modeling. This research aimed to recognize the migraine via using fuzzy logic and systems.Methods: A fuzzy expert system for diagnosis of migraine via Learning from Examples (LFE) algorithm was presented. Mamdani model was used in fuzzy inference engine using Max-Min as Or-And operators and Centroid method was used as defuzzification technique.Findings: Using the data of 148 patients, the migraine diagnostic system was trained by LFE algorithm and in average, 80 pieces of If-Then rules were produced for fuzzy system. The accuracy, precision, sensitivity, and specificity of the system were 97%, 80%, 70%, and 94%, respectively. Using the migraine diagnostic system by human experts, it was proved that the system had the ability of correct recognition by the rate of 81%.Conclusion: As the linguistic rules may be incomplete when human expert express their knowledge and according to importance of early diagnosis and favorable results, the LFE training algorithm is more effective than human experts system for recognition of migraine headache.

Keywords

References

  1. Sanchez E. Resolution of composite fuzzy relation equations. Inf Control 1976; 30(1): 38-48.
  2. Sanchez E. Truth-qualification and fuzzy relations in natural languages, application to medical diagnosis. Fuzzy Set Syst 1996; 84(2): 155-67.
  3. Sikchi SS, Sikchi S, Ali MS. Fuzzy expert systems (FES) for medical diagnosis. Int J Comput Appl 2013; 63(11): 7-16.
  4. Adlassnig K. Fuzzy set theory in medical diagnosis. IEEE Trans Syst Man Cybern 1986; 16(2): 260-5.
  5. Phuong NH, Kreinovich V. Fuzzy logic and its applications in medicine. Int J Med Inform 2001; 62(2-3): 165-73.
  6. De SK, Biswas R, Roy AR. An application of intuitionistic fuzzy sets in medical diagnosis. Fuzzy Set Syst 2001; 117(2): 209-13.
  7. Yao JF-F, Yao JS. Fuzzy decision making for medical diagnosis based on fuzzy number and compositional rule of inference. Fuzzy Set Syst 2001; 120(2): 351-66.
  8. Smets P. Medical diagnosis: Fuzzy sets and degrees of belief. Fuzzy Set Syst 1981; 5(3): 259-66.
  9. Kim YH, Kim SK, Oh SY, Ahn JY. A fuzzy differential diagnosis of headache. J Korean Data Inf Sci Soc 2007; 18(2): 429-38.
  10. Ahn JY, Kim YH, Kim SK. A Fuzzy differential diagnosis of headache applying linear regression method and fuzzy classification. IEICE T Inf Syst 2003; E86-D(12): 2790-3.
  11. Ahn JY, MUN KS, Kim YH, Oh SY, HAN BS. A Fuzzy Method for Medical Diagnosis of Headache. IEICE T Inf Syst 2008; E91-D(4): 1215-7.
  12. Burkhardt DG, Bonissone PP. Automated fuzzy knowledge base generation and tuning. Proceedings of 1st IEEE International Conference on Fuzzy Systems; 1992 Mar 8-12; San Diego, CA, USA. p. 179-88.
  13. Nomura H, Hayashi I, Wakami N. A learning method of fuzzy inference rules by descent method. Proceedings of 1st IEEE International Conference on Fuzzy Systems; 1992 Mar 8-12; San Diego, CA, USA. p. 203-10.
  14. Wang LX, Mendel JM. Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 2017; 22(6): 1414-27.
  15. Kia SM. Fuzzy logic using MATLAB. Tehran, Iran: Kian Rayane Sabz Publications; 2010. [In Persian].
  16. Siler W, Buckley JJ. Fuzzy expert systems and fuzzy reasoning. Hoboken, NJ: John Wiley and Sons, Inc.; 2005.
Journal of Isfahan Medical School
Volume 34, Issue 416 - Serial Number 416
November and December 2017
Pages 1680-1685

Files

History

  • Receive Date: 03 February 2017
  • Revise Date: 01 November 2024
  • Accept Date: 06 April 2022

Share

How to cite

Statistics