Authors:
Gerda Rodrigues de Oliveira
and
Cristiane Nobre
Affiliation:
Department of Computer Science, Pontifical Catholic University of Minas Gerais, PUC Minas - 500, Dom José Gaspar Street, Coração Eucarístico, Belo Horizonte, Brazil
Keyword(s):
COVID-19, Hospitalization, Machine Learning.
Abstract:
This work aims to verify the applicability of using Machine Learning techniques to predict hospitalization in confirmed cases of Covid-19. The study also intends to discover which attributes have the most significant impacts on hospitalization. The machine learning (ML) algorithms used in this experiment were Decision Tree, Random Forest, Neural Networks, and Naive Bayes. The data used for this experiment were made available by the government of Minas Gerais - Brazil, through open data. The model based on Random Forest obtained the best results, presenting the following metrics: Precision, Recall and F1-Score of 0.85, 0.84 and 0.84, respectively. In this experiment, essential characteristics for classifying the patient’s hospitalization are Comorbidity, Age Group, and HDI. The results point to a good predictive ability, demonstrating the potential use of ML techniques to predict the hospitalization of people by COVID-19.