Kernel Spherical K-Means and Support Vector Machine for Acute Sinusitis Classification

9th Annual Basic Science International Conference 2019

自然科学（总论）

Arfiani^1 ; Rustam, Zuherman^1 ; Pandelaki, Jacub^2 ; Siahaan, Arga^2

Department of Mathematics, University of Indonesia, Depok

16424, Indonesia^1

Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Jakarta

10430, Indonesia^2

关键词: Binary classification methods;    Data separation;    Generalization ability;    Higher dimensions;    Kernel function;    Machine learning methods;    Patient experiences;    Training data;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/546/5/052011/pdf
DOI  :  10.1088/1757-899X/546/5/052011

学科分类：自然科学（综合）

来源: IOP

【 摘 要 】

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%.