Tools and TechniquesDistinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms

- Computer-aided diagnosis for age-related cognitive decline based on cognitive tests.
- Some of the tests are Öktem Verbal Memory, semantic fluency and Boston naming tests.
- Completely separable from Alzheimer's disease with a success rate of 100%.
- Highly separable from mild cognitive impairment and vascular dementia.

Background and aimThis study aims to examine the distinguishability of age-related cognitive decline (ARCD) from dementias based on some neurocognitive tests using machine learning.Materials and methods106 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.ResultsThe 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).ConclusionThe findings show that machine learning can be successfully utilized for distinguishing ARCD from dementias based on neurocognitive tests.