Assessment of the performances of multilayer perceptron neural networks in comparison with recurrent neural networks and two statistical methods for diagnosing coronary artery disease

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dc.contributor.authorSuet, Necdet
dc.contributor.authorSenocak, Mustafa
dc.date.accessioned2024-06-12T11:09:05Z
dc.date.available2024-06-12T11:09:05Z
dc.date.issued2007
dc.departmentTrakya Üniversitesien_US
dc.description.abstractWe aimed to examine the diagnostic performances of multilayer perceptron neural networks (MLPNNs) for predicting coronary artery disease and to compare them with different types of artificial neural network methods, namely recurrent neural networks (RNNs) and two statistical methods (quadratic discriminant analysis (QDA) and logistic regression (LR)). MLPNNs were trained with backpropagation, quick propagation, delta-bar-delta and extended delta-bar-delta algorithms as classifiers; the RNN was trained with the Levenberg-Marquardt algorithm; LR and QDA were used for predicting coronary artery disease. Coronary artery disease was classified with accuracy rates varying from 79.9% to 83.9% by MLPNNs. Even though MLPNNs achieved higher accuracy rates than the statistical methods, LR (73.2%) and QDA (58.4%), their performances were lower compared to the RNN (84.7%). Among the four different types of training algorithms that trained MLPNNs, quick propagation achieved the highest accuracy rate; however, it was lower than the RNN trained with the Levenberg-Marquardt algorithm. RNNs, which demonstrated 84.7% accuracy and 86.5% positive predictive rates, may be a helpful tool in medical decision making for diagnosis of coronary artery disease.en_US
dc.identifier.doi10.1111/j.1468-0394.2007.00425.x
dc.identifier.endpage142en_US
dc.identifier.issn0266-4720
dc.identifier.issn1468-0394
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-34250201125en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage131en_US
dc.identifier.urihttps://doi.org/10.1111/j.1468-0394.2007.00425.x
dc.identifier.urihttps://hdl.handle.net/20.500.14551/22684
dc.identifier.volume24en_US
dc.identifier.wosWOS:000247125400001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofExpert Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNeural Networksen_US
dc.subjectMultilayer Perceptronen_US
dc.subjectTraining Algorithmsen_US
dc.subjectLogistic Regressionen_US
dc.subjectCoronary Artery Diseaseen_US
dc.subjectRisk-Factorsen_US
dc.subjectCardiovascular-Diseaseen_US
dc.subjectSignalsen_US
dc.subjectSystemen_US
dc.subjectEegen_US
dc.titleAssessment of the performances of multilayer perceptron neural networks in comparison with recurrent neural networks and two statistical methods for diagnosing coronary artery diseaseen_US
dc.typeArticleen_US

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