You are viewing a javascript disabled version of the site. Please enable Javascript for this site to function properly.
Go to headerGo to navigationGo to searchGo to contentsGo to footer
In content section. Select this link to jump to navigation

Multiple fuzzy c-means clustering algorithm in medical diagnosis

Issue title: Papers from the 3rd International Conference on Biomedical Engineering and Technology (iCBEB 2014), 25–28 September 2014, Beijing, China

Subtitle:

Guest editors: Edward J. Ciaccio

Article type: Research Article

Authors: Wu, Yanpinga; b; * | Duan, Huilonga | Du, Shufengc

Affiliations: [a] Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China | [b] Public Health Department, Zhejiang Medical College, Hangzhou, Zhejiang, China | [c] Zhejiang Dahua Technology Co., Ltd, Hangzhou, Zhejiang, China

Correspondence: [*] Corresponding author: Yanping Wu, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang 310029, China. Tel.: +86 15858153889; Fax: +86 0571 8769 2809; E-mail:[email protected],[email protected]

Keywords: Fuzzy c-means, medical diagnosis, primary headache

DOI: 10.3233/THC-150989

Journal: Technology and Health Care, vol. 23, no. s2, pp. S519-S527, 2015

Published: 2015
Get PDF open access

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.

Show more