Affiliations: [a] Programa de Pós-Graduação em Engenharia da Computação (PECS), Universidade Estadual do Maranhão (UEMA), São Luis, MA, Brazil | [b] Departamento de Computação (DCOMP), Instituto Federal do Maranhão (IFMA), São Luis, MA, Brazil
Correspondence:
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Corresponding author: Elaine Pinto Portela, Programa de Pós-Graduação em Engenharia da Computação (PECS), Universidade Estadual do Maranhão (UEMA), São Luis, MA, Brazil. E-mail: elainepportela@gmail.com.
Abstract: The world recently has faced the COVID-19 pandemic, a disease caused by the severe acute respiratory syndrome. The main features of this disease are the rapid spread and high-level mortality. The illness led to the rapid development of a vaccine that we know can fight against the virus; however, we do not know the actual vaccine’s effectiveness. Thus, the early detection of the disease is still necessary to provide a suitable course of action. To help with early detection, intelligent methods such as machine learning and computational intelligence associated with computer vision algorithms can be used in a fast and efficient classification process, especially using ensemble methods that present similar efficiency to traditional machine learning algorithms in the worst-case scenario. In this context, this review aims to answer four questions: (i) the most used ensemble technique, (ii) the accuracy those methods reached, (iii) the classes involved in the classification task, (iv) the main machine learning algorithms and models, and (v) the dataset used in the experiments.
