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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2021
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| Materias: | |
| 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 |