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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| Format: | Article |
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MDPI AG
2024-10-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/21/2372 |
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| author | Miguel-Angel Gil-Rios Ivan Cruz-Aceves Arturo Hernandez-Aguirre Martha-Alicia Hernandez-Gonzalez Sergio-Eduardo Solorio-Meza |
| author_facet | Miguel-Angel Gil-Rios Ivan Cruz-Aceves Arturo Hernandez-Aguirre Martha-Alicia Hernandez-Gonzalez Sergio-Eduardo Solorio-Meza |
| author_sort | Miguel-Angel Gil-Rios |
| collection | DOAJ |
| description | 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, which only consider the best individuals in a population, the proposed strategy also considers the worst individuals under certain conditions. In consequence, feature selection frequencies tend to be more uniform, decreasing the probability of premature convergent results and local-optima solutions. <b>Methods</b>: An image database containing 608 images, evenly balanced between positive and negative coronary stenosis cases, was used for experiments. A total of 473 features, including intensity, texture, and morphological types, were extracted from the image bank. A Support Vector Machine was employed to classify positive and negative stenosis cases, with Accuracy and the Jaccard Coefficient used as performance metrics. <b>Results</b>: The proposed strategy achieved a classification rate of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.92</mn></mrow></semantics></math></inline-formula> for Accuracy and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.85</mn></mrow></semantics></math></inline-formula> for the Jaccard Coefficient, obtaining a subset of 16 features, which represents a discrimination rate of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.97</mn></mrow></semantics></math></inline-formula> from the 473 initial features. <b>Conclusions</b>: The Hybrid Metaheuristic with explicit diversity control improved the classification performance of coronary stenosis cases compared to previous literature. Based on the achieved results, the identified feature subset demonstrates potential for use in clinical practice, particularly in decision-support information systems. |
| format | Article |
| id | doaj-art-9384d80a00414039a7009e0de82a2ddd |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-9384d80a00414039a7009e0de82a2ddd2025-08-20T02:13:16ZengMDPI AGDiagnostics2075-44182024-10-011421237210.3390/diagnostics14212372Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity ControlMiguel-Angel Gil-Rios0Ivan Cruz-Aceves1Arturo Hernandez-Aguirre2Martha-Alicia Hernandez-Gonzalez3Sergio-Eduardo Solorio-Meza4Universidad Área Académica de Tecnologías de la Información, Universidad Tecnológica de León, Blvd. Universidad Tecnológica 225, Col. San Carlos, León 37670, MexicoCONAHCYT, Consejo Nacional de Humanidades, Ciencia y Tecnología (CONAHCYT), Centro de Investigación en Matemáticas (CIMAT), A.C., Jalisco S/N, Col. Valenciana, Guanajuato 36000, MexicoCentro de Investigación en Matemáticas (CIMAT), A.C., Jalisco S/N, Col. Valenciana, Guanajuato 36000, MexicoUnidad Médica de Alta Especialidad (UMAE), Hospital de Especialidades No.1. Centro Médico Nacional del Bajio, Instituto Mexicano del Seguro Social (IMSS), Blvd. Adolfo López Mateos S/N, León 37150, MexicoDivisión de Ciencias e Ingenierías, Universidad de Guanajuato, Campus León, Loma del Bosque 103, Col. Lomas del Campestre, León 37150, MexicoThis 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, which only consider the best individuals in a population, the proposed strategy also considers the worst individuals under certain conditions. In consequence, feature selection frequencies tend to be more uniform, decreasing the probability of premature convergent results and local-optima solutions. <b>Methods</b>: An image database containing 608 images, evenly balanced between positive and negative coronary stenosis cases, was used for experiments. A total of 473 features, including intensity, texture, and morphological types, were extracted from the image bank. A Support Vector Machine was employed to classify positive and negative stenosis cases, with Accuracy and the Jaccard Coefficient used as performance metrics. <b>Results</b>: The proposed strategy achieved a classification rate of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.92</mn></mrow></semantics></math></inline-formula> for Accuracy and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.85</mn></mrow></semantics></math></inline-formula> for the Jaccard Coefficient, obtaining a subset of 16 features, which represents a discrimination rate of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.97</mn></mrow></semantics></math></inline-formula> from the 473 initial features. <b>Conclusions</b>: The Hybrid Metaheuristic with explicit diversity control improved the classification performance of coronary stenosis cases compared to previous literature. Based on the achieved results, the identified feature subset demonstrates potential for use in clinical practice, particularly in decision-support information systems.https://www.mdpi.com/2075-4418/14/21/2372coronary angiographyevolutionary algorithmfeature selectionpopulation diversitystenosis classification |
| spellingShingle | Miguel-Angel Gil-Rios Ivan Cruz-Aceves Arturo Hernandez-Aguirre Martha-Alicia Hernandez-Gonzalez Sergio-Eduardo Solorio-Meza Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control Diagnostics coronary angiography evolutionary algorithm feature selection population diversity stenosis classification |
| title | Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control |
| title_full | Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control |
| title_fullStr | Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control |
| title_full_unstemmed | Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control |
| title_short | Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control |
| title_sort | improving automatic coronary stenosis classification using a hybrid metaheuristic with diversity control |
| topic | coronary angiography evolutionary algorithm feature selection population diversity stenosis classification |
| url | https://www.mdpi.com/2075-4418/14/21/2372 |
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