2024-10-252024-10-252022-08-16LIMA, Marco Antonio Loureiro. Análise e classificação de severidade de COVID-19 usando aprendizado de máquina. Orientador: Diego Lisboa Cardoso. 2022. 51 f. Dissertação (Mestrado em Engenharia Elétrica) - Instituto de Tecnologia, Universidade Federal do Pará, Belém, 2022. Disponível em: https://repositorio.ufpa.br/jspui/handle/2011/16561. Acesso em:.https://repositorio.ufpa.br/jspui/handle/2011/16561In the last years, with the alarming growth of COVID-19 cases, a highly contagious viral disease, new forms of diagnosis and control for this sickness have become necessary to the spread decreases until the population is effectively vaccinated. In this context, Artificial Intelligence (AI) and its subfields appear as possible alternatives to help and provides a response to combat the virus. Some Machine Learning (ML) methods are shown as an answer to control this disease, these methods can perform an analysis based on a set of symptoms presented by the patient and consequently indicating the diagnosis, as well as streamline the treatment process. To achieve this goal in this paper, three models that uses ML methods to predict COVID-19 severity on different degrees are proposed, unlike other works whose purpose was to diagnose only the presence or absence of COVID-19, this paper aims to improve the classification of the patient’s disease state. The results in each of these models are evaluated through the metrics established in this work. Furthermore, there are distinct suggestions to improve the analysis and make predictions with greater accuracy..Acesso AbertoAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/Covid19Inteligência artificialAprendizado de máquinaArtificial intelligenceDissertaçãoCNPQ::ENGENHARIAS::ENGENHARIA ELETRICAINTELIGÊNCIA COMPUTACIONALCOMPUTAÇÃO APLICADA