Aprendizaje automático para clasificar pacientes infectados por COVID-19 sobre un conjunto de datos balanceado

Machine learning techniques can help identify infected patients, detect the spread of the virus, and analyze available patient information for better care and disease control. Random forest, Neural network, and Logistic regression using a data set with information from patients confirmed by COVID-19...

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Vydáno v:RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação číslo E58; s. 330 - 343
Hlavní autoři: Castillo, Carlos, Valdiviezo-Diaz, Priscila
Médium: Journal Article
Jazyk:španělština
Vydáno: Lousada Associação Ibérica de Sistemas e Tecnologias de Informacao 01.05.2023
Témata:
Algorithms
COVID-19
Datasets
Disease control
Machine learning
Neural networks
Variables
Viral diseases
ISSN:1646-9895
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Shrnutí:Machine learning techniques can help identify infected patients, detect the spread of the virus, and analyze available patient information for better care and disease control. Random forest, Neural network, and Logistic regression using a data set with information from patients confirmed by COVID-19 from a hospital. The results show that the random forest algorithm presents a better performance in the quality measures of Precision, Sensitivity, and Specificity with the balanced data set. Keywords: COVID-19; Balanced data set; Machine learning, Classification; Prediction. 1.
Bibliografie:ObjectType-Article-1
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ISSN:1646-9895