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Machine Learning Prediction of Hospitalization due to COVID-19 based on Self-Reported Symptoms: A Study for Brazil*

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posted on 09.02.2021, 16:05 by Igor Miranda, Gildeberto Cardoso, Madhurananda Pahar, Gabriel Oliveira, Thomas Niesler
Predicting the need for hospitalization due to COVID-19 may help patients to seek timely treatment and assist health professionals to monitor cases and allocate resources. We investigate the use of machine learning algorithms to predict the risk of hospitalization due to COVID-19 using the patient's medical history and self-reported symptoms, regardless of the period in which they occurred. Three datasets containing information regarding 217,580 patients from three different states in Brazil have been used. Decision trees, neural networks, and support vector machines were evaluated, achieving accuracies between 79.1% to 84.7%. Our analysis shows that better performance is achieved in Brazilian states ranked more highly in terms of the official human development index (HDI), suggesting that health facilities with better infrastructure generate data that is less noisy. One of the models developed in this study has been incorporated into a mobile app that is available for public use.

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Email Address of Submitting Author

igordantas@ufrb.edu.br

ORCID of Submitting Author

0000-0002-8555-9609

Submitting Author's Institution

Federal University of Reconcavo da Bahia

Submitting Author's Country

Brazil

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