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

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

Abstract 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 rel...

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Bibliographic Details
Main Authors: Pierre Michel, Nicolas Ngo, Jean-François Pons, Stéphane Delliaux, Roch Giorgi
Format: Article
Language:English
Published: BMC 2021-05-01
Series:BMC Medical Informatics and Decision Making
Subjects:
$$\gamma$$ γ -metric
Machine learning
Feature selection
Classification
Clinical decision making
Atrial fibrillation detection
Online Access:https://doi.org/10.1186/s12911-021-01427-8

Internet

https://doi.org/10.1186/s12911-021-01427-8

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