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

Zuherman Rustam (1), Jacub Pandelaki (2), Dea Aulia Utami (3), Rahmat Hidayat (4), Azizul Azhar Ramli (5)
(1) Department Mathematics, University of Indonesia, Kampus UI Depok, Depok,16424, Indonesia
(2) Department Radiology, FKUI/RSCM, Kampus UI Salemba,Jakarta,, 10430, Indonesia
(3) Department Mathematics, University of Indonesia, Kampus UI Depok, Depok,16424, Indonesia
(4) Department of Information Technology, Politeknik Negeri Padang, Indonesia
(5) Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Malaysia
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How to cite (IJASEIT) :
Rustam, Zuherman, et al. “Comparison Support Vector Machine and Fuzzy Possibilistic C-Means Based on the Kernel for Knee Osteoarthritis Data Classification”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 6, Dec. 2019, pp. 2142-6, doi:10.18517/ijaseit.9.6.9243.
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%.

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