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

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Journal of Isfahan Medical School
Volume 34, Issue 416 - Serial Number 416
November and December 2017
Pages 1680-1685

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History

  • Receive Date: 03 February 2017
  • Revise Date: 26 April 2024
  • Accept Date: 06 April 2022

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