Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets.

Dorn, Marcio; Grisci, Bruno Iochins; Narloch, Pedro Henrique; Feltes, Bruno César; Avila, Eduardo; Kahmann, Alessandro; Alho, Clarice Sampaio

Dorn, Marcio; Grisci, Bruno Iochins; Narloch, Pedro Henrique; Feltes, Bruno César; Avila, Eduardo; Kahmann, Alessandro; Alho, Clarice Sampaio.

Afiliación

Dorn M; Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
Grisci BI; Center of Biotechnology, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
Narloch PH; Forensic Science, National Institute of Science and Technology, Porto Alegre, RS, Brazil.
Feltes BC; Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
Avila E; Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
Kahmann A; Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
Alho CS; Department of Genetics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.

PeerJ Comput Sci ; 7: e670, 2021.

Article en En | MEDLINE | ID: mdl-34458574

Palabras clave

Covid; Data mining; Hemogram; Imbalanced datasets; Machine learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: PeerJ Comput Sci Año: 2021 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Estados Unidos

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