Classification of COVID-19 by using supervised optimized machine learning technique

Abstract

In recent two years, covid-19 diseases is the most harmful diseases in entire world. This disease increase the high mortality rate in several developed countries. Earlier identification of covid-19 symptoms can avoid the over illness or death. However, there are several researchers are introduced different methodology to identification of diseases symptoms. But, identification and classification of covid-19 diseases is the difficult task for every researchers and doctors. In this modern world, machine learning techniques is useful for several medical applications. This study is more focused in applying machine learning classifier model as SVM for classification of diseases. By improve the classification accuracy of the classifier by using hyper parameter optimization technique as modified cuckoo search algorithm. High dimensional data have unrelated, misleading features, which maximize the search space size subsequent in struggle to process data further thus not contributing to the learning practise, So we used a hybrid feature selection technique as mRMR (Minimum Redundancy Maximum Relevance) algorithm. The experiment is conducted by using UCI machine learning repository dataset. The classifier is conducted to classify the two set of classes such as COVID-19, and normal cases. The proposed model performance is analysed by using different parametric metrics, which are explained in result section.