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EMPLOYABILITY OF MACHINE LEARNING TOOLS AND TECHNIQUES IS EFFICACIOUS IN EARLY DETECTION AND DIAGNOSIS OF ALZHEIMER’S DISEASE

Updesh Sachdeva

31-38

Vol 15, Jan-Jun, 2022

Date of Submission: 2022-01-04 Date of Acceptance: 2022-03-12 Date of Publication: 2022-04-03

Abstract

Memory and other cognitive parts are significantly affected by Alzheimer's disease (AD). As there is no fix, early disease movement location and deferral are basic for overseeing Promotion. Verbal familiarity is one of the most widely recognized and delicate neuropsychological techniques utilized for identifying and assessing mental decreases in Promotion, in which a subject should name many things as conceivable in 30 or 60 seconds that have a place within a certain classification. A verb fluency (VF) task, a specific subset of verbal fluency that examines the subjects' list of verbs during a given period, is used in this study to develop a method for detecting AD. To identify the possibility of AD, we make use of natural language processing (NLP), random forest (RF), neural network (NN), and recurrent neural network (RNN) machine learning strategies. However, preprocessing the data is required for the developed models to stratify subjects into the appropriate AD and control groups with up to 76% accuracy using RF. This exactness is marginally lower, however not fundamentally, at 67% utilizing RNN and NLP, which includes practically no manual information preprocessing. Utilizing specific VF tasks for early AD detection is now possible, thanks to this study's results.

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