Distinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms

Basit Öğe Kaydı
dc.authorid0000-0003-2016-9965en_US
dc.authorid0000-0003-2016-9965en_US
dc.authorid0000-0003-3868-3137en_US
dc.authorid0000-0002-2887-9235en_US
dc.contributor.authorEr, Fusun
dc.contributor.authorIscen, Pinar
dc.contributor.authorSahin, Sevki
dc.contributor.authorCinar, Nilgun
dc.contributor.authorKarsidag, Sibel
dc.contributor.authorGoularas, Dionysis
dc.date.accessioned2024-07-12T21:50:38Z
dc.date.available2024-07-12T21:50:38Z
dc.date.issued2017en_US
dc.departmentMaltepe Üniversitesien_US
dc.description.abstractBackground and aim: This study aims to examine the distinguishability of age-related cognitive decline (ARCD) from dementias based on some neurocognitive tests using machine learning. Materials and methods: 106 subjects were divided into four groups: ARCD (n = 30), probable Alzheimer's disease (AD) (n = 20), vascular dementia (VD) (n = 21) and amnestic mild cognitive impairment (MCI) (n = 35). The following tests were applied to all subjects: The Wechsler memory scale-revised, a clock drawing, the dual similarities, interpretation of proverbs, word fluency, the Stroop, the Boston naming (BNT), the Benton face recognition, a copying-drawings and Oktem verbal memory processes (0VMPT) tests. A multilayer perceptron, a support vector machine and a classification via regression with M5-model trees were employed for classification. Results: The pairwise classification results show that ARCD is completely separable from AD with a success rate of 100% and highly separable from MCI and VD with success rates of 95.4% and 86.30%, respectively. The neurocognitive tests with the higher merit values were O-VMPT recognition (ARCD vs. AD), 0VMPT total learning (ARCD vs. MCI) and semantic fluency, proverbs, Stroop interference and naming BNT (ARCD vs. VD). Conclusion: The findings show that machine learning can be successfully utilized for distinguishing ARCD from dementias based on neurocognitive tests. (C) 2017 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipTUBITAK BIDEB [2211-C, 1649B031402382]en_US
dc.description.sponsorshipCurrent study was supported by TUBITAK BIDEB 2211-C (No: 1649B031402382).en_US
dc.identifier.doi10.1016/j.jocn.2017.03.021
dc.identifier.endpage192en_US
dc.identifier.issn0967-5868
dc.identifier.issn1532-2653
dc.identifier.pmid28347685en_US
dc.identifier.scopus2-s2.0-85016816641en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage186en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.jocn.2017.03.021
dc.identifier.urihttps://hdl.handle.net/20.500.12415/8169
dc.identifier.volume42en_US
dc.identifier.wosWOS:000405535800039en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoenen_US
dc.publisherELSEVIER SCI LTDen_US
dc.relation.ispartofJOURNAL OF CLINICAL NEUROSCIENCEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY01847
dc.subjectAge-related cognitive declineen_US
dc.subjectDementia, machine learningen_US
dc.subjectMild cognitive impairmenten_US
dc.titleDistinguishing age-related cognitive decline from dementias: A study based on machine learning algorithmsen_US
dc.typeArticle
dspace.entity.typePublication

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu