Acute sinusitis data classification using grey wolf optimization-based support vector machine

Ajeng Maharani Putri (University of Indonesia)
Zuherman Rustam (University of Indonesia)
Jacub Pandelaki (Cipto Mangunkusumo Hospital)
Ilsya Wirasati (University of Indonesia)
Sri Hartini (University of Indonesia)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 10, No 2: June 2021

Acute sinusitis is the most common form of sinusitis, and it causes swelling and inflammation within the nose. The main thing that can causes sinusitis is probably due to viruses, and also can be caused by other factors, namely bacteria, fungi, irritation, dust, and allergens. In this research, the CT scan data attributes will be used for classification and grey wolf optimization-support vector machine (GWO-SVM) will be the machine learning technique used, where the GWO technique will be used to tuned the parameters in SVM. The performance of methods was analyzed using the python programming language with different percentages of training data, which started from 10% to 90%. The GWO-SVM method proposed provides better accuracy than using SVM without GWO.