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

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 p...

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Bibliographic Details
Published inApplied sciences Vol. 12; no. 18; p. 8939
Main Authors Andrade, Evandro Carvalho de, Pinheiro, Plácido Rogerio, Barros, Ana Luiza Bessa de Paula, Nunes, Luciano Comin, Pinheiro, Luana Ibiapina C. C., Pinheiro, Pedro Gabriel Calíope Dantas, Holanda Filho, Raimir
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2022
Subjects
Algorithms
Artificial intelligence
Classification
Comparative analysis
Coronaviruses
COVID-19
Decision making
Epidemiology
Evolution
Learning algorithms
Machine learning
medical diagnosis optimisation
Multilayer perceptrons
Patients
prediction
Prediction models
Public health
Risk analysis
Risk factors
Severe acute respiratory syndrome
Severe acute respiratory syndrome coronavirus 2
Support vector machines
Surveillance systems
Brazil
United States > US
China
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12188939