Multiple fuzzy c-means clustering algorithm in medical diagnosis.

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.

[1]  Ujjwal Maulik,et al.  Genetic clustering for automatic evolution of clusters and application to image classification , 2002, Pattern Recognit..

[2]  H. Irshad,et al.  Image segmentation using fuzzy clustering: A survey , 2010, 2010 6th International Conference on Emerging Technologies (ICET).

[3]  P. Ritchey,et al.  Quantifying Headache Symptoms and Other Headache Features From Chart Notes , 2004, Headache.

[4]  M. Karnan,et al.  Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[5]  B. Kulkarni,et al.  An ant colony approach for clustering , 2004 .

[6]  Witold Pedrycz,et al.  An interval weighed fuzzy c-means clustering by genetically guided alternating optimization , 2014, Expert Syst. Appl..

[7]  Lin Sun,et al.  An ensemble feature selection technique for cancer recognition. , 2014, Bio-medical materials and engineering.

[8]  Tzung-Pei Hong,et al.  FUZZY C-MEANS IN FINDING SUBTYPES OF CANCERS IN CANCER DATABASE , 2014 .

[9]  Che Wun Chiou,et al.  A novel fuzzy c-means method for classifying heartbeat cases from ECG signals , 2010 .

[10]  Aboul Ella Hassanien,et al.  Fuzzy and hard clustering analysis for thyroid disease , 2013, Comput. Methods Programs Biomed..

[11]  Qiang Niu,et al.  An improved fuzzy C-means clustering algorithm based on PSO , 2011, J. Softw..

[12]  Kaijian Xia,et al.  Research in Clustering Algorithm for Diseases Analysis , 2013, J. Networks.

[13]  Lin Sun,et al.  Feature selection using mutual information based uncertainty measures for tumor classification. , 2014, Bio-medical materials and engineering.

[14]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[15]  Hichem Frigui,et al.  Unsupervised learning of prototypes and attribute weights , 2004, Pattern Recognit..

[16]  Songul Albayrak,et al.  FUZZY C-MEANS CLUSTERING ON MEDICAL DIAGNOSTIC SYSTEMS , 2003 .

[17]  Jeong-Yong Ahn,et al.  AN APPLICATION OF INTERVAL-VALUED INTUITIONISTIC FUZZY SETS FOR MEDICAL DIAGNOSIS OF HEADACHE , 2011 .

[18]  Kemal Polat,et al.  Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering , 2012, Int. J. Syst. Sci..

[19]  F. Ahmed.  Headache disorders: differentiating and managing the common subtypes , 2012, British journal of pain.

[20]  S Nithya,et al.  An Ant Colony Clustering Algorithm Using Fuzzy Logic , 2012, SOCO 2012.

[21]  Svetlana Simic,et al.  Computer-Assisted Diagnosis of Primary Headaches , 2008, HAIS.

[22]  K. Mengersen,et al.  Linkage and heritability analysis of migraine symptom groupings: a comparison of three different clustering methods on twin data , 2009, Human Genetics.

[23]  Wang Li,et al.  Fuzzy C Mean Algorithm Based on Feature Weights , 2006 .

[24]  S. R. Kannan,et al.  Extended fuzzy c-means: an analyzing data clustering problems , 2012, Cluster Computing.

[25]  Hong Jia,et al.  Feature Weighted Kernel Clustering with Application to Medical Data Analysis , 2013, Brain and Health Informatics.