Comparison Support Vector Machine and Fuzzy Possibilistic C-Means based on the kernel for Knee Osteoarthritis data Classification

Zuherman Rustam, Jacub Pandelaki, Dea Aulia Utami, Rahmat Hidayat, Azizul Azhar Ramli

Abstract


Osteoarthritis is a chronic joint disease that occurs when the protective cartilage that cushions the ends of bones wears down over time and fails to be repaired. The common form of the disease is knee osteoarthritis while it can affect all body parts with joints, such as hands, ankles, hips, and spine. The major cause of knee osteoarthritis is the continuous depletion of its cartilage. During the diagnosis, machine learning is used because early prevention is necessary for proper treatment. This study, therefore, considers classification methods of Support Vector Machine (SVM) and clustering methods using fuzzy clusterings such as Fuzzy C-Means (FCM), Fuzzy Possibilistic C-Means (FPCM), and Fuzzy Possibilistic C-Means based on kernel (FPCMK) to analyze of knee osteoarthritis. SVM is a machine learning technique that works based on the principle of structural risk minimization (SRM) to obtain the best hyperplane to separate two or more classes in input space. Otherwise, the fuzzy clustering is to determine the value of a distance and to know and measure the similarity of each object to be observed. FPCMK uses the kernel Radial Base Function (RBF) in the fuzzy clustering method. The kernel function is applicable for handling non-separable data problems. This method will be compared to the level of the measured parameter; their accuracy, recall, precision, and f1 score. The greatest level of accuracy is generated from SVM with an accuracy value of 86.7%, then followed by FPCMK with an accuracy value of 85.5%.

Keywords


fuzzy C-Means clustering; fuzzy possibilistic C-Means; FPCMK; osteoarthritis knee; support vector machine.

Full Text:

PDF

References


Gale Encyclopedia of Medicine, (2008), Key facts retrieved from

http://medical dictionary.thefreedictionary.com/osteoarthritis on 22 August 2018.

Lawrence RC, Felson DT, Helmick CG, Arnold LM, Choi H, Deyo RA, et al. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States. Part II. Arthritis Rheum. (2008), 58(1), 26-35.

Arden N, Blanco F, Cooper C, Guermazi A, Hayashi D, Hunter D, et al. Introduction: Historical and Current Perspective on Osteoarthritis. Atlas of Osteoarthritis Springer Healthcare Ltd.; 2015. p.10-9

S.R.Kannan, R.Devi, S.Ramathilagam, T.P.Hong, A.Ravikumar, “Robust fuzzy clustering algorithms in analyzing high dimensional cancer databases”, 2015, Journal Applied Soft Computing vol.35, pp. 199-213.

N.R.Pal, K.Pal, J.M.Keller, J.C.Bezdek, “A Possibilistic Fuzzy c-Means Clustering Algorithm”, 2005, IEEE Transactions on Fuzzy Systems, vol.13, No.4.

C.Zhang, Y.Zhou, T.Martin, “Similarity-Based Fuzzy and Possibilistic c-means Algorithm”, 2008, Proceedings of the 11th Joint Conference on Information Sciences.

K.J.Dsouza, Z.A.Ansari, “Experimental Exploration of Support Vector Machine for Cancer Cell Classification”, 2017, IEEE International Conference on Cloud Computing in Emerging Markets.

Z. Rustam and D. Zahras. Comparison between Support Vector Machine and Fuzzy C-means as a classifier for the intrusion detection system. 2nd International Conference on Statistics, Mathematics, Teaching, and Research. (2018).

Pal, N. R., Kal, P., dan Bezdek, J. C. 1997. A Mixed C Means Clustering Model. IEEE, 11- 21.

Z. Rustam and F. Yaurita. Insolvency prediction in insurance companies using Support Vector Machines and Fuzzy Kernel C-Means. 2nd International Conference on Statistics, Mathematics, Teaching, and Research. (2018).

Z. Rustam and N. P. A. A. Ariantari. Support Vector Machines for Classifying Policyholders Satisfactorily in Automobile Insurance. 2nd International Conference on Statistics, Mathematics, Teaching, and Research. (2018).

Wang, L. (2005). Support Vector Machines: Theory and Applications. New York: Springer, 177.

Xue, H., Yang, Q., & Chen, S. (2009). SVM: Support Vector Machines. United States of America: Taylor & Francis Group.

Wu Xindong and Kumar Vipin. The Top Ten Algorithms in Data Mining (Taylor and Francis Group, LLC, 2009), 127-150.




DOI: http://dx.doi.org/10.18517/ijaseit.9.6.9243

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development