An Optimized Machine Learning Algorithms for COVID-19 Disease

The rapid growth of coronavirus disease (COVID-19) has become a worldwide problem, with over 15 million individuals infected by July 2020. Clinical imaging, including X-ray imaging, may be used to stop this epidemic. Based on chest X-ray imagery, this study aimed to distinguish between normal, COVID-19COVID-19, and pneumonitis patients. As a result, this chapter introduces a few machine learning and nature-inspired metaheuristic techniques to achieve a globally optimum solution. Metaheuristic techniques are used to improve the hyperparametersHyperparameters of machine-learning algorithms. Various machine learning algorithms, such as Naive Bayes, Support Vector Machine, KNN, and metaheuristic hybrid model (PSO-SVM, GA-SVM), are explained in this chapter. Moreover, machine learning techniques were tested and evaluated by comparing them to other classification algorithms using a publicly available dataset, including normal, COVID-19, and pneumonitis X-ray images. In the context of convergence rate towards the global optimum, the output of metaheuristic techniques is more effective than that of a basic machine and deep learning techniques.