Classification of Mild Cognitive Impairment Subtypes using Neuropsychological Data

While the research on Alzheimer’s disease (AD) is progressing, timely intervention before an individual becomes demented is often emphasized. Mild Cognitive Impairment (MCI), which is thought of as a prodromal syndrome to AD, may be useful in this context as potential interventions can be applied to individuals at increased risk of developing dementia. The current study attempts to address this problem using a selection of machine learning algorithms to discriminate between cognitively normal individuals and MCI individuals among a cohort of community dwelling individuals aged 70-90 years based on neuropsychological test performance. The overall best algorithm in our experiments was AdaBoost with decision trees while random forests was consistently stable. Ten-fold cross validation was used with ten repetitions to reduce variability and assess generalizing capabilities of the trained models. The results presented are consistently of the same calibre or better than the limited number of similar studies reported in the literature.