title: Distinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms.
abstract: OBJECTIVE
  This study aims to examine the distinguishability of age-related cognitive decline (ARCD) from dementias based on some neurocognitive tests using machine learning.
  
  
  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 Öktem verbal memory processes (Ö-VMPT) 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 Ö-VMPT recognition (ARCD vs. AD), Ö-VMPT total learning (ARCD vs. MCI) and semantic fluency, proverbs, Stroop interference and naming BNT (ARCD vs. VD).
  
  
  CONCLUSIONS
  The findings show that machine learning can be successfully utilized for distinguishing ARCD from dementias based on neurocognitive tests.
authors.affiliation: Department of Biotechnology, Graduate School of Natural and Applied Sciences, Yeditepe University, Istanbul, Turkey. Electronic address: fusun.er@std.yeditepe.edu.tr.
authors.forename: Füsun
authors.initials: F
authors.lastname: Er
authors.affiliation: Department of Neuroscience, Experimental Medicine and Research Institute, Istanbul University, Istanbul, Turkey.
authors.forename: Pınar
authors.initials: P
authors.lastname: Iscen
authors.affiliation: Department of Neurology, Medical School, Maltepe University, Istanbul, Turkey.
authors.forename: Sevki
authors.initials: S
authors.lastname: Sahin
authors.affiliation: Department of Neurology, Medical School, Maltepe University, Istanbul, Turkey.
authors.forename: Nilgun
authors.initials: N
authors.lastname: Çinar
authors.affiliation: Department of Neurology, Medical School, Maltepe University, Istanbul, Turkey.
authors.forename: Sibel
authors.initials: S
authors.lastname: Karsidag
authors.affiliation: Department of Computer Engineering, Faculty of Engineering, Yeditepe University, Istanbul, Turkey.
authors.forename: Dionysis
authors.initials: D
authors.lastname: Goularas
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis*; D060825:Cognitive Dysfunction / Q000175:diagnosis*; D015140:Dementia, Vascular / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male
keywords: Age-related cognitive decline; Dementia, machine learning; Mild cognitive impairment