ABSTRACT
This work makes use of artificial intelligence to contribute with empirical evidence that help predict death by COVID-19, enabling the improvement of health protocols used in health systems in Brazil and providing society with more tools to combat COVID-19. Data from January to September 2021 for Brazil are used in order to predict death by COVID-19 based on the clinical status of patients who used the Unified Health System in the studied period, in which three classification algorithms were tried: Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). The LR, DT, and RF models had a mean accuracy of 76%, 76%, and 77% in predicting death, respectively. In addition, it was possible to infer that when patients reach a point that require the use of ventilatory support and ICU, added to age, their chance of dying of COVID-19 is greater.
KEYWORDS
COVID-19; SARS-CoV-2; Logistic models; Artificial intelligence; Machine learning