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

Ajeng Maharani Putri, Zuherman Rustam, Jacub Pandelaki, Ilsya Wirasati, Sri Hartini


Acute sinusitis is the main manifestation of sinusitis, which causes inflammation and swelling of spaces inside 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 the machine learning method that will be used is Grey Wolf Optimization-Support Vector Machine (GWO-SVM), 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.


Acute sinusitis; Grey wolf optimization; GWO-SVM; Meta-heuristic; Support vector machine



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