Multiple fuzzy c-means clustering algorithm in medical diagnosis

Technology and Health Care

Volumn 23, Issue , 2015, Pages S519-S527

  Wu, Yanping ^a,b   Duan, Huilong ^a   Du, Shufeng ^c  

^a ZHEJIANG UNIVERSITY   (China)

^b ZHEJIANG UNIVERSITY SCHOOL OF MEDICINE   (China)

^c Huadian Electric Power Research Institute Co   (China)

Author keywords

Fuzzy c means; medical diagnosis; primary headache

Indexed keywords

CLUSTER ANALYSIS; CONFERENCE PAPER; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC PROCEDURE; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE CLASSIFICATION; FUZZY LOGIC; FUZZY SYSTEM; GENETIC ALGORITHM; HUMAN; INTERMETHOD COMPARISON; MIGRAINE; MULTIPLE FUZZY C MEAN ALGORITHM; PATIENT CODING; PATIENT INFORMATION; PRIMARY HEADACHE; PRIORITY JOURNAL; PROCESS OPTIMIZATION; TENSION HEADACHE; TRIGEMINAL AUTONOMIC CEPHALALGIA; ALGORITHM; CLASSIFICATION; COMPUTER ASSISTED DIAGNOSIS; DIFFERENTIAL DIAGNOSIS; HEADACHE; PROCEDURES;

ALGORITHMS; CLUSTER ANALYSIS; DIAGNOSIS, COMPUTER-ASSISTED; DIAGNOSIS, DIFFERENTIAL; FUZZY LOGIC; HEADACHE; HUMANS;

EID: 84937690685     PISSN: 09287329     EISSN: None     Source Type: Journal    
DOI: 10.3233/THC-150989     Document Type: Conference Paper

References (28)

1. Albayrak S, Amasyali F. Fuzzy c-means clustering on medical diagnostic systems. Turkish symposium on Artificial Intelligence and Neural Networks. 2003; 1-3.
2. Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences. 1984; 10(2): 191-203.
3. Naz S, Majeed H, Irshad H. Image segmentation using fuzzy clustering: A survey. Sixth International Conference on Emerging Technologies. 2010.
4. Xu JC, Sun L, Gao YP, Xu TH. An ensemble feature selection technique for cancer recognition. Bio-medical Materials and Engineering. 2014; 24: 1001-1008. doi: 10. 3233/BME-130897.
5. Sun L, Xu JC. Feature selection using mutual information based uncertainty measures for tumor classification. Biomedical Materials and Engineering. 2014; 24: 763-770. doi: 10. 3233/BME-130865.
6. Azar AT, El-Said SA, Hassanien AE. Fuzzy and hard clustering analysis for thyroid disease. Computer Method and Programs in Biomedicine. 2013; 111: 1-16. doi: 10. 1016/j. cmpb. 2013. 01. 002.
7. Xia KJ, Wu Y, Ren XG. Research in clustering algorithm for disease analysis. Journal of Networks. 2013; 8(7): 1632-1639. doi: 10. 4304/jnw. 8. 7. 1632-1639.
8. Jia H, Cheung YM. Feature weighted kernel clustering with application to medical data analysis. Conference on Brain and Health Informatics. 2013. p. 496-505.
9. Bandyopadhyay S, Maulik U. Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition. 2002; 35: 1197-1208.
10. Shelokar PS, Jayaraman VK, Kulkarni BD. An ant colony approach for clustering. Analytica Chimica Acta. 2004; 509: 187-195. doi: 10. 1016/j. aca. 2003. 12. 032.
11. Niu Q, Huang XJ. An improved fuzzy c-means clustering algorithm based on PSO. Journal of Software. 2011; 6(5): 873-879. doi: 10. 4304/jsw. 6. 5. 873-879.
12. Frigui H, Nasraoui O. Unsupervised learning of prototypes and attribute weights. Pattern Recognition. 2004; 37: 567-581. doi: 10. 1016/j. patcog. 2003. 08. 002.
13. Yeh YC, Wang WJ, Chiou CW. A novel fuzzy c-means method for classifying heartbeat cases from ECG signals. Measurement. 2010; 43: 1542-1555. doi: 10. 1016/j. measurement. 2010. 08. 019.
14. Gopal NN, Karnan M. Diagnose brain tumor through MRI using image processing clustering algorithms such as fuzzy c means along with intelligent optimization techniques. IEEE International Conference on Computational Intelligence and Computing Research. 2010.
15. Nithya S, Manavalan R. An ant colony clustering algorithm using fuzzy logic. International Journal of Soft Computing and Software Engineering. 2012; 2(5): 13-24. doi: 10. 7321/jscse. v2. n5. 2.
16. Ramathilagam S, Devi R, Kannan SR. Extended fuzzy c-means: An analyzing data clustering problems. Cluster Computing-The Journal of Networks Software Tools and Applications. 2013; 16: 389-406. doi: 10. 1007/s10586-012-0202-2.
17. Kannan SR, Ramthilagam S, Devi R, Hong TP. Fuzzy c-means in finding subtypes of cancers in cancer database. Journal of Innovative Optical Health Sciences. 2014; 7(1): 1-20. doi: 10. 1142/S1793545814500187.
18. Wang LJ, Guan SY, Wang XL, Wang XZ. Fuzzy c-mean algorithm based on feature weights. Chinese Journal of Computers. 2006; 29(10): 1797-1803.
19. Polat K. Classification of Parkinson's disease using feature weighting method. International Journal of Systems Science. 2012; 43(4): 597-609. doi: 10. 1080/00207721. 2011. 581395.
20. Zhang LY, Pedrycz W, Lu W, Liu XD, Zhang L. An interval weighted fuzzy c-means clustering by genetically guided alternating optimization. Expert Systems with Application. 2014; 41: 5960-5971. doi: 10. 1016/j. eswa. 2014. 03. 042.
21. Pal NR, Bazdek JC. On clusters validity for the fuzzy c-means mode. IEEE Trans. On Fuzzy Systems. 1995; 3(3): 370-379.
22. Fayyaz A. Headache disorders: differentiating and managing the common subtypes. The British pain society. 2012; 6(3): 124-132. doi: 10. 1177/2049463712459691.
23. Headache Classification Committee of the International Headache Society. The International Classification of Headache Disorders (3rd edition, beta version). 2013. doi: 10. 1177/0333102413485658.
24. Chen CCM, Mengersen KL, Keith JM, Martin NG, Nyholt DR. Linkage and heritability analysis of migraine symptom groupings: A comparison of three different clustering methods on twin data. Journal of Human Genetics. 2009; 125: 591-604. doi: 10. 1007/s00439-009-0652-7.
25. Svetlana S, Dragan S, Petar S. Computer-assisted diagnosis of primary headache. HAIS. 2008. p. 314-321.
26. Jeong Y, Kyung SH, Sun YO. An application of interval-valued intuitionistic fuzzy sets for medical diagnosis of headache. International Journal of Innovative Computing Information and Control. 2011; 7(5): 2755-2762.
27. Lora A, Hasse P, Neal R, Robert S. Quantifying headache symptoms and other headache features from chart notes. Headache. 2004; 44: 873-884.
28. Zhuang HY, Shen XP, Lu JF, Huang LF. Theoretical analyses of the computing method of fuzzy cluster. Journal of Jiangsu Agriculture College. 1998; 19: 37-41.