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A novel combined dynamic ensemble selection model for imbalanced data to detect COVID-19 from complete blood count
BACKGROUND: As blood testing is radiation-free, low-cost and simple to operate, some researchers use machine learning to detect COVID-19 from blood test data. However, few studies take into consideration the imbalanced data distribution, which can impair the performance of a classifier. METHOD: A no...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier B.V.
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479386/ https://www.ncbi.nlm.nih.gov/pubmed/34614451 http://dx.doi.org/10.1016/j.cmpb.2021.106444 |