Applied Sciences (Sep 2022)

Towards Machine Learning Algorithms in Predicting the Clinical Evolution of Patients Diagnosed with COVID-19

  • Evandro Carvalho de Andrade,
  • Plácido Rogerio Pinheiro,
  • Ana Luiza Bessa de Paula Barros,
  • Luciano Comin Nunes,
  • Luana Ibiapina C. C. Pinheiro,
  • Pedro Gabriel Calíope Dantas Pinheiro,
  • Raimir Holanda Filho

DOI
https://doi.org/10.3390/app12188939
Journal volume & issue
Vol. 12, no. 18
p. 8939

Abstract

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Predictive modelling strategies can optimise the clinical diagnostic process by identifying patterns among various symptoms and risk factors, such as those presented in cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus (COVID-19). In this context, the present research proposes a comparative analysis using benchmarking techniques to evaluate and validate the performance of some classification algorithms applied to the same dataset, which contains information collected from patients diagnosed with COVID-19, registered in the Influenza Epidemiological Surveillance System (SIVEP). With this approach, 30,000 cases were analysed during the training and testing phase of the prediction models. This work proposes a comparative approach of machine learning algorithms (ML), working on the knowledge discovery task to predict clinical evolution in patients diagnosed with COVID-19. Our experiments show, through appropriate metrics, that the clinical evolution classification process of patients diagnosed with COVID-19 using the Multilayer Perceptron algorithm performs well against other ML algorithms. Its use has significant consequences for vital prognosis and agility in measures used in the first consultations in hospitals.

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