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dc.contributor.authorTunay, Mustafa
dc.contributor.authorPashaei, Elnaz
dc.contributor.authorPashaei, Elham
dc.date.accessioned2023-10-18T16:52:59Z
dc.date.available2023-10-18T16:52:59Z
dc.date.issued2022en_US
dc.identifier.issn1687-5265
dc.identifier.issn1687-5273
dc.identifier.urihttps://hdl.handle.net/11363/5944
dc.description.abstractThe hypercube optimization search (HOS) approach is a new efficient and robust metaheuristic algorithm that simulates the dove's movement in quest of new food sites in nature, utilizing hypercubes to depict the search zones. In medical informatics, the classification of medical data is one of the most challenging tasks because of the uncertainty and nature of healthcare data. This paper proposes the use of the HOS algorithm for training multilayer perceptrons (MLP), one of the most extensively used neural networks (NNs), to enhance its efficacy as a decision support tool for medical data classification. The proposed HOS-MLP model is tested on four significant medical datasets: orthopedic patients, diabetes, coronary heart disease, and breast cancer, to assess HOS's success in training MLP. For verification, the results are compared with eleven different classifiers and eight well-regarded MLP trainer metaheuristic algorithms: particle swarm optimization (PSO), biogeography-based optimizer (BBO), the firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), bat algorithm (BAT), monarch butterfly optimizer (MBO), and the flower pollination algorithm (FPA). The experimental results demonstrate that the MLP trained by HOS outperforms the other comparative models regarding mean square error (MSE), classification accuracy, and convergence rate. The findings also reveal that the HOS help the MLP to produce more accurate results than other classification algorithms for the prediction of diseases.en_US
dc.language.isoengen_US
dc.publisherHINDAWI LTD, ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON W1T 5HF, ENGLANDen_US
dc.relation.isversionof10.1155/2022/1612468en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.titleHybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classificationen_US
dc.typearticleen_US
dc.relation.ispartofComputational Intelligence and Neuroscienceen_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.authoridhttps://orcid.org/0000-0001-8843-621Xen_US
dc.authoridhttps://orcid.org/0000-0001-9391-9785en_US
dc.authoridhttps://orcid.org/0000-0001-7401-4964en_US
dc.identifier.volume2022en_US
dc.identifier.startpage1en_US
dc.identifier.endpage16en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorTunay, Mustafa
dc.contributor.institutionauthorPashaei, Elham


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