A three-stage hybrid clustering system for diagnosing children with primary headache disorder

Headache disorders can be considered as the predominant neurological condition. In the field of neurological diseases, migraine was estimated to cost a total of euro27 billion per year for the loss through reduced work productivity in the European Community. Medical data and information in turn provide knowledge based on which physicians make scientific decisions for diagnosis and treatments. It is, therefore, very useful to create diagnostic tools to help physicians make better decisions. This paper is focused on a new hybrid clustering system combining analytic hierarchy process and weighted fuzzy c-means clustering method for diagnosing children with primary headache disorder. The proposed three-stage hybrid diagnosing system is tested on data set collected from hospitalized children in the Clinical Centre of Vojvodina, Novi Sad, Serbia.

Automated summary

This paper introduces a new hybrid clustering system for diagnosing children with primary headache disorder. By combining analytic hierarchy process and weighted fuzzy c-means clustering method, the system can assist physicians in making more accurate diagnoses.