A metaheuristic approach based on coronavirus herd immunity optimiser for breast cancer diagnosis

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dc.contributor.authorHosseinalipour, Ali
dc.contributor.authorGhanbarzadeh, Reza
dc.contributor.authorArasteh, Bahman
dc.contributor.authorGharehchopogh, Farhad Soleimanian
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2024-05-19T14:39:37Z
dc.date.available2024-05-19T14:39:37Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractAs one of the important concepts in epidemiology, herd immunity was recommended to control the COVID-19 pandemic. Inspired by this technique, the Coronavirus Herd Immunity Optimiser has recently been introduced, demonstrating promising results in addressing optimisation problems. This particular algorithm has been utilised to address optimisation problems widely; However, there is room for enhancement in its performance by making modifications to its parameters. This paper aims to improve the Coronavirus Herd Immunity Optimisation algorithm to employ it in addressing breast cancer diagnosis problem through feature selection. For this purpose, the algorithm was discretised after the improvements were made. The Opposition-Based Learning approach was applied to balance the exploration and exploitation stages to enhance performance. The resulting algorithm was employed in the diagnosis of breast cancer, and its performance was evaluated on ten benchmark functions. According to the simulation results, it demonstrates superior performance in comparison with other well-known approaches of the similar nature. The results demonstrate that the new approach performs well in diagnosing breast cancer with high accuracy and less computational complexity and can address a variety of real-world optimisation problems.en_US
dc.identifier.doi10.1007/s10586-024-04360-3
dc.identifier.issn1386-7857
dc.identifier.issn1573-7543
dc.identifier.scopus2-s2.0-85191081143en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.1007/s10586-024-04360-3
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4816
dc.identifier.wosWOS:001207077400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofCluster Computing-The Journal of Networks Software Tools and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectBreast Canceren_US
dc.subjectDiagnosisen_US
dc.subjectMetaheuristicen_US
dc.subjectFeature Selectionen_US
dc.subjectCoronavirusen_US
dc.subjectHerd Immunityen_US
dc.titleA metaheuristic approach based on coronavirus herd immunity optimiser for breast cancer diagnosisen_US
dc.typeArticleen_US

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