Open AccessKernel Spherical K-Means and Support Vector Machine for Acute Sinusitis Classification
Author(s) -
Arfiani ArFiani,
Zuherman Rustam,
Jacub Pandelaki,
Arga Siahaan
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/546/5/052011
Subject(s) - support vector machine , artificial intelligence , generalization , kernel (algebra) , radial basis function kernel , binary classification , pattern recognition (psychology) , computer science , sinusitis , sinus (botany) , mathematics , machine learning , medicine , kernel method , surgery , mathematical analysis , botany , combinatorics , biology , genus
Acute sinusitis is an inflammation of the sinus which causes the cavity around the sinus to swells due to accumulated mucus. It makes the patient experience difficulty in breathing through the nose. Generally, it is caused by the common cold, and in most cases, the patient recovers within seven to ten days. However, persistent acute sinusitis can cause severe infections and other complications. Therefore, it requires timely detection and more accurate method of classification. Many techniques have been used to classify acute sinusitis but, in this study, the machine learning methods which includes Kernel Spherical K-Means (KSPKM) and Support Vector Machine (SVM) was applied. SPKM is the application of K-Means, in this research, it was modified by changing the inner product with kernel function to ensure linear data separation on higher dimensions for the maximization of SPKM performance. The SVM is a binary classification method that helps to create a model with good generalization ability. We used CT scan result data from RSCM, Central Jakarta. Simulations were performed with different percentage of training data. The results were compared in terms of Accuracy and Running Time. The score showed that the performance of KSPKM attained an accuracy rate of 97%, while SVM reached 90%.