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OPTIMIZING THE COMBINATION OF NEUROPSYCHOLOGICAL TESTS FOR EFFECTIVE COGNITIVE IMPAIRMENT CLASSIFICATION

      There have been studies about decision rules guiding the clinical diagnosis of Alzheimer’s dementia (Jack et al. 2018, Bennett et al. 2006). Alzheimer’s dementia poses great socioeconomic burden for the world. Our aim is to assess the potential of machine learning approaches for classification of healthy (HV), mild cognitively impaired (MCI) and Alzheimer’s dementia individuals using neuropsychological tests; while optimizing the combination of tests that can be affordably implemented expeditiously and accurately in the community to classify and diagnose Alzheimer’s dementia.
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