Füsun Er1, Pınar Iscen2, Sevki Sahin3, Nilgun Çinar3, Sibel Karsidag3, Dionysis Goularas4. 1. Department of Biotechnology, Graduate School of Natural and Applied Sciences, Yeditepe University, Istanbul, Turkey. Electronic address: fusun.er@std.yeditepe.edu.tr. 2. Department of Neuroscience, Experimental Medicine and Research Institute, Istanbul University, Istanbul, Turkey. 3. Department of Neurology, Medical School, Maltepe University, Istanbul, Turkey. 4. Department of Computer Engineering, Faculty of Engineering, Yeditepe University, Istanbul, Turkey.
Abstract
BACKGROUND 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 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). CONCLUSION: The findings show that machine learning can be successfully utilized for distinguishing ARCD from dementias based on neurocognitive tests.
BACKGROUND 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 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). CONCLUSION: The findings show that machine learning can be successfully utilized for distinguishing ARCD from dementias based on neurocognitive tests.
Authors: Omar Ibrahim; Heidi G Sutherland; Rodney A Lea; Fatima Nasrallah; Neven Maksemous; Robert A Smith; Larisa M Haupt; Lyn R Griffiths Journal: J Mol Med (Berl) Date: 2021-11-19 Impact factor: 4.599