Early Detection of Alzheimer’s Disease Using Patient Neuropsychological and Cognitive Data and Machine Learning Techniques

Abstract

Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia in older adults. With no known cures, there is a pressing need to find behavioral tasks and biomarkers that can accurately assess and/or predict disease progression in asymptotic patients, as treatment is likely to be most effective at an early stage of AD. On the other hand, artificial intelligence systems are powerful and critical tools to support early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation in healthcare. In this study, standard neuropsychological tests and a simple 5.5-minute cognitive task were administered to patients with mild AD or mild cognitive impairment (MCI) (AD group, n=28) and cognitively normal older adults (Control group, n=50). Patients with mild AD or MCI were collapsed together as the AD group. Four different machine learning algorithms were applied to classify patients from healthy controls using the data collected from neuropsychological tests, or the cognitive task, or both. The results of the study revealed that machine learning technique has the potential to assist AD diagnosis using the neuropsychological data, and when combining the neuropsychological and cognitive data, the classification accuracy can be further improved.

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