Pordeus, DanielDanielPordeusRibeiro, PedroPedroRibeiroZacarias, LailaLailaZacariasPaulo Madeiro, JoaoJoaoPaulo MadeiroLobo Marques, Joao AlexandreJoao AlexandreLobo MarquesMiguel Rodrigues, PedroPedroMiguel RodriguesLeite, CamilaCamilaLeiteAlves Neto, ManoelManoelAlves NetoAires Peixoto Jr, ArnaldoArnaldoAires Peixoto Jrde Oliveira, AdrielAdrielde OliveiraLobo Marques, Joao AlexandreFong, Simon James2024-04-022024-04-022023978-3-031-30788-1https://dspace.usj.edu.mo/handle/123456789/5669The continuous development of robust machine learning algorithms in recent years has helped to improve the solutions of many studies in many fields of medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the severity of COVID-19 from electrocardiogram (ECG) signals. We retrospectively collected 100 5-minute ECGs from 50 patients in two different positions, upright and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.enBook Section