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

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.