Feature Selection and Detection of COPD Using Automatic Programming Methods

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dc.contributor.authorKaraca, Huseyin
dc.contributor.authorArslan, Sibel
dc.date.accessioned2025-05-04T16:41:59Z
dc.date.available2025-05-04T16:41:59Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 21 September 2024 through 22 September 2024 -- Malatya -- 203423
dc.description.abstractChronic obstructive pulmonary disease (COPD) is a serious lung disease that severely limits patients' quality of life and can lead to further health complications if it is not diagnosed and treated in time. In this study, various Automatic Programming (AP) methods, including Genetic Programming (GP) and Multi-Gene Genetic Programming (MGGP), are used to achieve highly accurate predictions for diagnosis. Among the methods, MGGP stands out with a prediction accuracy of 100%. The results highlight the potential of AP methods in modeling complex nonlinear relationships in COPD data and identifying key features that influence the diagnosis of the disease. In addition, the effectiveness and efficiency of AP methods suggest that they can contribute to the development of early diagnosis and treatment strategies. © 2024 IEEE.
dc.identifier.doi10.1109/IDAP64064.2024.10710964
dc.identifier.isbn979-833153149-2
dc.identifier.scopus2-s2.0-85207860130
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP64064.2024.10710964
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35019
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250504
dc.subjectAutomatic Programming
dc.subjectChronic Obstructive Pulmonary Disease
dc.subjectGenetic Programming
dc.subjectMultigene Genetic Programming
dc.subjectPulmonary Disease Modeling
dc.titleFeature Selection and Detection of COPD Using Automatic Programming Methods
dc.typeConference Object

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