Impact of metaheuristic iteration on artificial neural network structure in medical data

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dc.contributor.authorSalman, İhsan
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorBayat, Oğuz
dc.contributor.authorShaker, Khalid
dc.date.accessioned2021-05-15T12:41:48Z
dc.date.available2021-05-15T12:41:48Z
dc.date.issued2018
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionShaker, Khalid/0000-0001-9108-5553; Salman, Ihsan/0000-0002-0974-4271
dc.description.abstractMedical data classification is an important factor in improving diagnosis and treatment and can assist physicians in making decisions about serious diseases by collecting symptoms and medical analyses. In this work, hybrid classification optimization methods such as Genetic Algorithm (GA), Particle Swam Optimization (PSO), and Fireworks Algorithm (FWA), are proposed for enhancing the classification accuracy of the Artificial Neural Network (ANN). The enhancement process is tested through two experiments. First, the proposed algorithms are applied on five benchmark medical data sets from the repository of the University of California in Irvine (UCI). The model with the best results is then used in the second experiment, which focuses on tuning the parameters of the selected algorithm by choosing a different number of iterations in ANNs with different numbers of hidden layers. Enhanced ANN with the three optimization algorithms are tested on biological gene sequence big dataset obtained from The Cancer Genome Atlas (TCGA) repository. GA and FWA are statistically significant but PSO was statistically not, and GA overcame PSO and FWA in performance. The methodology is successful and registers improvements in every step, as significant results are obtained.en_US
dc.identifier.doi10.3390/pr6050057
dc.identifier.issn2227-9717
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85047408683
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/pr6050057
dc.identifier.urihttps://hdl.handle.net/20.500.12939/856
dc.identifier.volume6en_US
dc.identifier.wosWOS:000435197800020
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorSalman, İhsan
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorBayat, Oğuz
dc.language.isoen
dc.publisherMdpien_US
dc.relation.ispartofProcesses
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassificationen_US
dc.subjectMetaheuristic Algorithmsen_US
dc.subjectANNen_US
dc.subjectPSOen_US
dc.subjectFWAen_US
dc.subjectGAen_US
dc.subjectData Miningen_US
dc.titleImpact of metaheuristic iteration on artificial neural network structure in medical data
dc.typeArticle

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