title: Multiple fuzzy c-means clustering algorithm in medical diagnosis.
abstract: BACKGROUND
  In recent years, the use of the fuzzy c-means (FCM) clustering techniques in medical diagnosis has steadily increased, because of its effectiveness in recognizing systems in the medical database to help medical experts diagnosing diseases. However, its performance is highly dependent on the randomly initialized cluster centroids which may allow the diagnosis to be trapped into the problem of the local optimum.
  
  
  OBJECTIVE
  This paper proposes a multiple fuzzy c-means (MFCM) algorithm for medical diagnosis.
  
  
  METHODS
  The new method optimizes the initial optimizing cluster centers by comparing the Euclidean distance between patient data. Further, this paper assigns a set of weights to the features of a certain disease to equalize their difference influence as a substitute for data normalization.
  
  
  RESULTS
  The performance of proposed MFCM algorithm was demonstrated through dividing complex primary headache data into Migraine, Tension-Type Headache (TTH), Trigeminal Autonomic Cephalalgias (TACs) and other primary headache disorders. In addition the superiority of MFCM algorithm was proven by comparing analytical results with other state-of-the-art clustering methods.
  
  
  CONCLUSIONS
  This MFCM method has shown a new application in medical diagnosis.
authors.affiliation: Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.|Public Health Department, Zhejiang Medical College, Hangzhou, Zhejiang, China.
authors.forename: Yanping
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
authors.forename: Huilong
authors.initials: H
authors.lastname: Duan
authors.affiliation: Zhejiang Dahua Technology Co., Ltd, Hangzhou, Zhejiang, China.
authors.forename: Shufeng
authors.initials: S
authors.lastname: Du
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D017143:Fuzzy Logic; D006261:Headache / Q000145:classification* / Q000175:diagnosis*; D006801:Humans
keywords: Fuzzy c-means; medical diagnosis; primary headache