Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/14865
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dc.contributor.authorKuntalp, Damla Gurkan-
dc.contributor.authorOzcan, Nermin-
dc.contributor.authorDuzyel, Okan-
dc.contributor.authorKababulut, Fevzi Yasin-
dc.contributor.authorKuntalp, Mehmet-
dc.date.accessioned2024-10-25T23:18:50Z-
dc.date.available2024-10-25T23:18:50Z-
dc.date.issued2024-
dc.identifier.issn2075-4418-
dc.identifier.urihttps://doi.org/10.3390/diagnostics14192244-
dc.identifier.urihttps://hdl.handle.net/11147/14865-
dc.description.abstractThe correct diagnosis and early treatment of respiratory diseases can significantly improve the health status of patients, reduce healthcare expenses, and enhance quality of life. Therefore, there has been extensive interest in developing automatic respiratory disease detection systems. Most recent methods for detecting respiratory disease use machine and deep learning algorithms. The success of these machine learning methods depends heavily on the selection of proper features to be used in the classifier. Although metaheuristic-based feature selection methods have been successful in addressing difficulties presented by high-dimensional medical data in various biomedical classification tasks, there is not much research on the utilization of metaheuristic methods in respiratory disease classification. This paper aims to conduct a detailed and comparative analysis of six widely used metaheuristic optimization methods using eight different transfer functions in respiratory disease classification. For this purpose, two different classification cases were examined: binary and multi-class. The findings demonstrate that metaheuristic algorithms using correct transfer functions could effectively reduce data dimensionality while enhancing classification accuracy.en_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectmetaheuristicen_US
dc.subjectfeature selectionen_US
dc.subjectrespiratory disease classificationen_US
dc.titleA Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classificationen_US
dc.typeArticleen_US
dc.departmentIzmir Institute of Technologyen_US
dc.identifier.volume14en_US
dc.identifier.issue19en_US
dc.identifier.wosWOS:001331764800001-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.3390/diagnostics14192244-
dc.identifier.pmid39410648-
dc.identifier.wosqualityQ2-
dc.identifier.scopusqualityQ2-
dc.description.woscitationindexScience Citation Index Expanded-
item.grantfulltextnone-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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