Different medical data mining approaches based prediction of ischemic stroke

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dc.authoridARSLAN, Ahmet Kadir/0000-0001-8626-9542
dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authoridSarihan, Mehmet Ediz/0000-0002-1266-4213
dc.authorwosidARSLAN, Ahmet Kadir/AAA-2409-2020
dc.authorwosidSarıhan, Mehmet Ediz/JMQ-5971-2023
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.contributor.authorArslan, Ahmet Kadir
dc.contributor.authorColak, Cemil
dc.contributor.authorSarihan, Mehmet Ediz
dc.date.accessioned2024-08-04T20:41:38Z
dc.date.available2024-08-04T20:41:38Z
dc.date.issued2016
dc.departmentİnönü Üniversitesien_US
dc.description.abstractAim: Medical data mining (also called knowledge discovery process in medicine) processes for extracting patterns from large datasets. In the current study, we intend to assess different medical data mining approaches to predict ischemic stroke. Materials and methods: The collected dataset from Turgut Ozal Medical Centre, Inonu University, Malatya, Turkey, comprised the medical records of 80 patients and 112 healthy individuals with 17 predictors and a target variable. As data mining approaches, support vector machine (SVM), stochastic gradient boosting (SGB) and penalized logistic regression (PLR) were employed. 10-fold cross validation resampling method was utilized, and model performance evaluation metrics were accuracy, area under ROC curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The grid search method was used for optimizing tuning parameters of the models. Results: The accuracy values with 95% CI were 0.9789 (0.9470-0.9942) for SVM, 0.9737 (0.9397-0.9914) for SGB and 0.8947 (0.8421-0.9345) for PLR. The AUC values with 95% CI were 0.9783 (0.9569-0.9997) for SVM, 0.9757 (0.9543-0.9970) for SGB and 0.8953 (0.8510-0.9396) for PLR. Conclusions: The results of the current study demonstrated that the SVM produced the best predictive performance compared to the other models according to the majority of evaluation metrics. SVM and SGB models explained in the current study could yield remarkable predictive performance in the classification of ischemic stroke. (C) 2016 Elsevier Ireland Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2016.03.022
dc.identifier.endpage92en_US
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.pmid27208524en_US
dc.identifier.scopus2-s2.0-84962288912en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage87en_US
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2016.03.022
dc.identifier.urihttps://hdl.handle.net/11616/97247
dc.identifier.volume130en_US
dc.identifier.wosWOS:000376507300010en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods and Programs in Biomedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIschemic strokeen_US
dc.subjectMedical data miningen_US
dc.subjectPenalized logistic regressionen_US
dc.subjectStochastic gradient boostingen_US
dc.subjectSupport vector machineen_US
dc.titleDifferent medical data mining approaches based prediction of ischemic strokeen_US
dc.typeArticleen_US

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