Prediction of mortality in stroke patients using multilayer perceptron neural networks

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dc.contributor.authorSut, Necdet
dc.contributor.authorCelik, Yahya
dc.date.accessioned2024-06-12T11:08:46Z
dc.date.available2024-06-12T11:08:46Z
dc.date.issued2012
dc.departmentTrakya Üniversitesien_US
dc.description.abstractAim: We aimed to predict mortality in stroke patients by using multilayer perceptron (MLP) neural networks. Materials and methods: A data set consisting of 584 stroke patients was analyzed using MLP neural networks. The effect of prognostic factors (age, hospitalization time, sex, hypertension, atrial fibrillation, embolism, stroke type, infection, diabetes mellitus, and ischemic heart disease) on mortality in stroke were trained with 6 different MLP algorithms [quick propagation (QP), Levenberg-Marquardt (LM), backpropagation (BP), quasi-Newton (QN), delta bar delta (DBD), and conjugate gradient descent (CGD)]. The performances of the MLP neural network algorithms were compared using the receiver operating characteristic (ROC) curve method. Results: Among the 6 algorithms that were trained with the MLP, QP achieved the highest specificity (81.3%), sensitivity (78.4%), accuracy (80.7%), and area under the curve (AUC) (0.869) values, while CGD achieved the lowest specificity (61.5%), sensitivity (58.7%), accuracy (60.8%), and AUC (0.636) values. The AUC of the QP algorithm was statistically significantly higher than the AUCs of the QN, DBD, and CGD algorithms (P < 0.05 for all of the pairwise comparisons). Conclusion: The MLP trained with the QP algorithm achieved the highest specificity, sensitivity, accuracy, and AUC values. This can be helpful in the prediction of mortality in stroke.en_US
dc.identifier.doi10.3906/sag-1105-20
dc.identifier.endpage893en_US
dc.identifier.issn1300-0144
dc.identifier.issn1303-6165
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-84864083161en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage886en_US
dc.identifier.trdizinid142011en_US
dc.identifier.urihttps://doi.org/10.3906/sag-1105-20
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/142011
dc.identifier.urihttps://hdl.handle.net/20.500.14551/22563
dc.identifier.volume42en_US
dc.identifier.wosWOS:000308053300020en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal Of Medical Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMultilayer Perceptron Neural Networksen_US
dc.subjectStrokeen_US
dc.subjectMortalityen_US
dc.subjectAlgorithmen_US
dc.subjectDiseaseen_US
dc.subjectClassificationen_US
dc.subjectDiagnosisen_US
dc.subjectSignalsen_US
dc.subjectSystemen_US
dc.titlePrediction of mortality in stroke patients using multilayer perceptron neural networksen_US
dc.typeArticleen_US

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