Kernel Based Fuzzy C-Means Clustering for Chronic Sinusitis Classification

Abstract

Sinusitis is an inflammation of the sinus wall, a small cavity interconnected through the airways in the skull bones. It is located on the back of the forehead, inside the cheek bone structure, on both sides of the nose, and behind the eyes. Chronic sinusitis is caused by infection, growth of nasal polyps, or irregularities of the nasal septum. This condition can affect teenagers, adults, and even children. To classify sinusitis we use Kernel Based Fuzzy C-Means (FCM) Clustering Algorithm, which is the development of Fuzzy C-Means (FCM) Algorithm. FCM is one of the widely used clustering technique. FCM algorithm comprises of sample points used to make whole and sub vector spaces according to the size of the distance. However, when non-linear data is separated, the convergence is inaccurate and slow. To overcome this problem, a Kernel-Based Fuzzy C-Means algorithm that makes use of kernel functions as a substitute for Euclidean distance utilized. It maps out samples to high-dimensional space to increase the differences between cluster centres, so they can overcome FCM deficiencies and improve linear machine capabilities. Data was obtained from the laboratory of Radiology at Cipto Mangunkusumo National General Hospital, Indonesia, with a 100% accuracy.