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J Med Signals Sens. 2019 Aug 29;9(3):174-180. doi: 10.4103/jmss.JMSS_47_18. eCollection 2019 Jul-Sep.

Diagnosis of Common Headaches Using Hybrid Expert-Based Systems.

Author information

1
Department of Mathematics, Payame Noor University, Tehran, Iran.
2
Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
3
Department of Mathematics, School of Mathematics, Mashhad Payame Noor University, Mashhad, Iran.
4
Department of Neurology, Faculty of Medicine, Neurology School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Abstract

Background:

Headache is one of the most common forms of medical complaints with numerous underlying causes and many patterns of presentation. The first step for starting the treatment is the recognition stage. In this article, the problem of primary and secondary headache diagnosis is considered, and we evaluate the use of intelligence techniques and soft computing in order to predict the diagnosis of common headaches.

Methods:

A fuzzy expert-based system for the diagnosis of common headaches by Learning-From-Examples (LFE) algorithm is presented, in which Mamdani model was used in fuzzy inference engine using Max-Min as Or-And operators, and the Centroid method was used as defuzzification technique. In addition, this article has analyzed common headache using two classification techniques, and headache diagnosis based on a support vector machine (SVM) and multilayer perceptron (MLP)-based method has been proposed. The classifiers were used to recognize the four types of common headache, namely migraine, tension, headaches as a result of infection, and headaches as a result of increased intra cranial presser.

Results:

By using a dataset obtained from 190 patients, suffering from primary and secondary headaches, who were enrolled from a medical center located in Mashhad, the diagnostic fuzzy system was trained by LFE algorithm, and on an average, 123 pieces of If-Then rules were produced for fuzzy system, and it was observed that the system had the ability of correct recognition by a rate of 85%. Using the headache diagnostic system by MLP- and SVM-based decision support system, the accuracy of classification into four types improved by 88% when using the MLP and by 90% with the SVM classifier. The performance of all methods is evaluated using classification accuracy, precision, sensitivity, and specificity.

Conclusion:

As the linguistic rules may be incomplete when human experts express their knowledge, and according to the proximity of common headache symptoms and importance of early diagnosis, the LFE training algorithm is more effective than human expert system. Favorable results obtained by the implementation and evaluation of the suggested medical decision support system based on the MLP and SVM show that intelligence techniques can be very useful for the recognition of common headaches with similar symptoms.

KEYWORDS:

Headache; Learning-From-Example algorithm; multilayer perceptron; recognition; support vector machines

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