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