A hybrid fuzzy clustering approach for diagnosing primary headache disorder. (11th September 2020)

• Record Type: Journal Article
• Authors: Simić, Svetlana; Banković, Zorana; Villar, José R; Simić, Dragan; Simić, Svetislav D
• Is Part Of: Logic journal of the IGPL. Volume 29:Number 2(2021)
• Page Start: 220
• Page End: 235
• Publication Date: 2020-09-11
• Subjects: Data clustering -- number of clusters -- maximum likelihood estimation clustering -- fuzzy partition method -- Calinski–Harabasz index; Logic, Symbolic and mathematical -- Periodicals; 511.3
• DOI: 10.1093/jigpal/jzaa048
• Languages: English
• ISSNs: 1367-0751

Abstract

Abstract: Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It has been studied in various research fields: data mining, machine learning, pattern recognition and in engineering, economics and biomedical data analysis. Headache is not a disease that typically shortens one's life, but it can be a serious social as well as a health problem. Approximately 27 billion euros per year are lost through reduced work productivity in the European community. This paper is focused on a new strategy based on a hybrid model for combining fuzzy partition method and maximum likelihood estimation clustering algorithm for diagnosing primary headache disorder. The proposed hybrid system is tested on two data sets for diagnosing headache disorder collected from Clinical Centre of Vojvodina in Serbia.