Pordeus, DanielDanielPordeusRibeiro, PedroPedroRibeiroZacarias, LailaLailaZacariasde Oliveira, AdrielAdrielde OliveiraLobo Marques, Joao AlexandreJoao AlexandreLobo MarquesRodrigues, Pedro MiguelPedro MiguelRodriguesLeite, CamilaCamilaLeiteNeto, Manoel AlvesManoel AlvesNetoPeixoto, Arnaldo AiresArnaldo AiresPeixotodo Vale Madeiro, Joao PauloJoao Paulodo Vale Madeiro2024-04-022024-04-022023978-3-031-34953-9https://dspace.usj.edu.mo/handle/123456789/533910.1007/978-3-031-34953-9_40The COVID-19 pandemic has posed a significant public health challenge on a global scale. It is imperative that we continue to undertake research in order to identify early markers of disease progression, enhance patient care through prompt diagnosis, identification of high-risk patients, early prevention, and efficient allocation of medical resources. In this particular study, we obtained 100 5-min electrocardiograms (ECGs) from 50 COVID-19 volunteers in two different positions, namely upright and supine, who were categorized as either moderately or critically ill. We used classification algorithms to analyze heart rate variability (HRV) metrics derived from the ECGs of the volunteers with the goal of predicting the severity of illness. Our study choose a configuration pro SVC that achieved 76% of accuracy, and 0.84 on F1 Score in predicting the severity of Covid-19 based on HRV metrics.EnglishCOVID-19signal processingdisease severity classificationElectrocardiogram (ECG)Heart Rate Variability (HRV)text::conference output::conference proceedings::conference paper