Predicting COVID-19 hospitalizations with attribute selection based on genetic and classification algorithms release_iwhujnvf7bc4vlgrx5d5jhbq2i

by Miriam Pizzatto Colpo, Bruno Cascaes Alves, Kevin Soares Pereira, Anna Flávia Zimmermann Brandão, Marilton Sanchotene de Aguiar, Tiago Thompsen Primo

Published in iSys by Sociedade Brasileira de Computacao - SB.

2022   Volume 15

Abstract

The COVID-19 pandemic has been pressuring the whole society and overloading hospital systems. Machine learning models designed to predict hospitalizations, for example, can contribute to better targeting hospital resources. However, as the excess of information, often irrelevant or redundant, can impair predictive models' performance, we propose a hybrid approach to attribute selection in this work. This method aims to find an optimal attribute subset through a genetic algorithm, which considers the results of a classification model in its evaluation function to improve the hospitalization need prediction of COVID-19 patients. We evaluated this approach in two official databases from the State Health Secretariat of Rio Grande do Sul, covering COVID-19 cases registered up to October 2020 and June 2021, respectively. As a result, we provided an increase of 18% in the classification precision for patients with hospitalization necessities in the first database, while in the second one, considering a temporal evaluation with sliding window, this gain was on average 6%. In a real-time application, this would also mean greater precision in targeting resources and, consequently and mainly, improved service to the infected population.
In application/xml+jats format

Archived Files and Locations

application/pdf  1.0 MB
file_56tn6osxyfh6nlotimbqg7bmt4
sol.sbc.org.br (publisher)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2022-06-29
Container Metadata
Open Access Publication
In DOAJ
Not in Keepers Registry
ISSN-L:  1984-2902
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: c33d3bde-8c51-41dc-8a95-f30a711f5e37
API URL: JSON