فهرست مطالب monireh khayamnia

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.
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%.
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.

Headache is one of the most common medical complaints.The treatment types of headaches require the diagnosis of these types.The aim of this study was to distinguish and diagnose common headaches by fuzzy logic and fuzzy system.
A fuzzy expert system for the distinguish types of common headaches is presented, the Mamdani modeL was used in fuzzy inference engine using Max-Min as OR-AND operators and Centroid method was used as defuzzification technique.
The fuzzy system was evaluated using data obtained from 150 patients and showed 82% good agreement and high ability in terms of correct diagnosis. Accuracy, Precision, sensitivity, specificity of the system were 86%, 93%, 85%, 88% for migraine , 93%, 91%, 55%, 99% for tension, 97%, 86%, 66%, 99% for headaches resulting from infection and 95%, 85%, 88%, 97% for headaches resulting from increase of ICP, respectively.The Binomial and Chi-Square Test evaluated that between correct and incorrect diagnosis was meaningful difference and the proportion of correct diagnosis (82%) was more than the incorrect diagnosis (18%) (PAccording to proximity of common headaches symptoms, and importance of early diagnosis of headache, and favorable results of the implementation and evaluation of the suggested expert system, therefore this system can be very useful for diagnosis of common headaches with similar symptoms.