High-Dimensional Feature Selection for Automatic Classification of Coronary Stenosis Using an Evolutionary Algorithm

High-Dimensional Feature Selection for Automatic Classification of Coronary Stenosis Using an Evolutionary Algorithm

In this paper, a novel strategy to perform high-dimensional feature selection using an evolutionary algorithm for the automatic classification of coronary stenosis is introduced. The method involves a feature extraction stage to form a bank of 473 features considering different types such as intensi...

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Bibliographic Details
Main Authors: Miguel-Angel Gil-Rios, Ivan Cruz-Aceves, Arturo Hernandez-Aguirre, Ernesto Moya-Albor, Jorge Brieva, Martha-Alicia Hernandez-Gonzalez, Sergio-Eduardo Solorio-Meza
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Diagnostics
Subjects:
bank of features
coronary angiograms
evolutionary algorithm
feature selection
K-nearest neighbor
stenosis classification
Online Access:https://www.mdpi.com/2075-4418/14/3/268

Internet

https://www.mdpi.com/2075-4418/14/3/268

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