Er, FusunIscen, PinarSahin, SevkiCinar, NilgunKarsidag, SibelGoularas, Dionysis2024-07-122024-07-1220170967-58681532-265310.1016/j.jocn.2017.03.0212-s2.0-85016816641https://dx.doi.org/10.1016/j.jocn.2017.03.021https://hdl.handle.net/20.500.12415/8169Background 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.eninfo:eu-repo/semantics/closedAccessAge-related cognitive declineDementia, machine learningMild cognitive impairmentArticle19228347685Q218642WOS:000405535800039Q4