A filter approach for feature selection in classification: application to automatic atrial fibrillation detection in electrocardiogram recordings

BACKGROUND: In high-dimensional data analysis, the complexity of predictive models can be reduced by selecting the most relevant features, which is crucial to reduce data noise and increase model accuracy and interpretability. Thus, in the field of clinical decision making, only the most relevant fe...

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Detalles Bibliográficos
Autores principales: Michel, Pierre, Ngo, Nicolas, Pons, Jean-François, Delliaux, Stéphane, Giorgi, Roch
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8094578/
https://www.ncbi.nlm.nih.gov/pubmed/33947379
http://dx.doi.org/10.1186/s12911-021-01427-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8094578/
https://www.ncbi.nlm.nih.gov/pubmed/33947379
http://dx.doi.org/10.1186/s12911-021-01427-8

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