Predicting 10-day Mortality in Patients with Strokes Using Neural Networks and Multivariate Statistical Methods

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dc.contributor.authorCelik, Guner
dc.contributor.authorBaykan, Omer K.
dc.contributor.authorKara, Yakup
dc.contributor.authorTireli, Hulya
dc.date.accessioned2020-03-26T18:58:25Z
dc.date.available2020-03-26T18:58:25Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractBackground: The aim of the present study was to evaluate the performance of 2 different multivariate statistical methods and artificial neural networks (ANNs) in predicting the mortality of hemorrhagic and ischemic patients within the first 10 days after stroke. Methods: The multilayer perceptron (MLP) ANN model and multivariate statistical methods (multivariate discriminant analysis [MDA] and logistic regression analysis [LRA]) have been used to predict acute stroke mortality. The data of total 570 patients (230 hemorrhagic and 340 ischemic stroke), who were admitted to the hospital within the first 24 hours after stroke onset, have been used to develop prediction models. The factors affecting the prognosis were used as inputs for prediction models. Survival or death status of the patients was taken as output of the models. Results: For the MLP method, the accuracies were 99.9% in a training data set and 80.9% in a testing data set for the hemorrhagic group, whereas 97.8% and 75.9% for the ischemic group, respectively. For the MDA method, the training and testing performances were 89.8%, 87.8% and 80.6%, 79.7% for hemorrhagic and ischemic groups, respectively. For the LRA method, the training and testing performances for the hemorrhagic group were 89.7% and 86.1%, and for the ischemic group were 81.7% and 80.9%, respectively. Conclusions: Training and test performances yielded different results for ischemic and hemorrhagic groups. MLP method was most successful for the training phase, whereas LRA and MDA methods were successful for the test phase. In the hemorrhagic group, higher prediction performances were achieved for both training and testing phases. (C) 2014 by National Stroke Associationen_US
dc.identifier.doi10.1016/j.jstrokecerebrovasdis.2013.12.018en_US
dc.identifier.endpage1512en_US
dc.identifier.issn1052-3057en_US
dc.identifier.issn1532-8511en_US
dc.identifier.issue6en_US
dc.identifier.pmid24674954en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1506en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013.12.018
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31078
dc.identifier.volume23en_US
dc.identifier.wosWOS:000338475600036en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.ispartofJOURNAL OF STROKE & CEREBROVASCULAR DISEASESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectPredicting outcomeen_US
dc.subjectischemic strokeen_US
dc.subjecthemorrhagic strokeen_US
dc.subjectmodelsen_US
dc.subjectstatisticalen_US
dc.titlePredicting 10-day Mortality in Patients with Strokes Using Neural Networks and Multivariate Statistical Methodsen_US
dc.typeArticleen_US

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