Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control

Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control

This study proposes a novel Hybrid Metaheuristic with explicit diversity control, aimed at finding an optimal feature subset by thoroughly exploring the search space to prevent premature convergence. <b>Background/Objectives</b>: Unlike traditional evolutionary computing techniques, whic...

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Bibliographic Details
Main Authors: Miguel-Angel Gil-Rios, Ivan Cruz-Aceves, Arturo Hernandez-Aguirre, Martha-Alicia Hernandez-Gonzalez, Sergio-Eduardo Solorio-Meza
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Diagnostics
Subjects:
coronary angiography
evolutionary algorithm
feature selection
population diversity
stenosis classification
Online Access:https://www.mdpi.com/2075-4418/14/21/2372
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