Acute sinusitis classification using support and fuzzy support vector machines

Abstract

Abstract The medical sector is currently in need of a method to aid in the classification of diseases, which contemporarily progresses into varying types. Therefore, the role of technology is highly relevant in the process of overcoming this challenge. This report discusses acute sinusitis, which is one of the most common forms of sinusitis, possibly caused by viruses, bacteria, fungi, pollutants, allergies, and also autoimmune reactions. Furthermore, the Support Vector Machines (SVM) and Fuzzy Support Vector Machines (FSVM) are used as a classification method to diagnose a person of acute sinusitis, therefore, this research aims to compare how both work, using Radial Basis Function (RBF) and Polynomial Kernel. Data of CT scan from Cipto Mangunkusumo Hospital, Indonesia was used to evaluate acute sinusitis, in terms of Accuracy, Sensitivity, Precision, and F1-Score. Thus, the final results indicate a better performance for FSVM than SVM in all perspectives, especially using the RBF kernel.