Machine Learning for Prognosis of Patients with COVID-19: An Early Days Analysis - Figuerêdo, José Solenir L.; Araújo-Calumby, Renata F.; Calumby, Rodrigo T. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2021: Anais do XVIII Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2021.18241, 2021)
This work proposes a machine learning approach to predict the prognosis of patients with COVID-19. To assist in this task, a descriptive analysis and relative risk estimation were performed. In addition, the importance of variables in the perspective of machine learning algorithms was computed and discussed. The experiments were performed with large-scale nation-wide dataset from Brazil. The results reveal that the model developed was able to predict the patient's prognosis with an AUC = 0.8382. The results also point out that the chance of death is greater among patients over 60 years old, with comorbidities, and symptoms such as dyspnea and Oxygen saturation (< 95%), confirming results observed in other regions of the world.