Alzheimer’s Disease (AD) is normally assessed in clinical settings using neuropsychological tests and medical procedures such as neuroimaging techniques: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) among others. The latter procedures are expensive and unavailable in most nations, so early diagnosis of AD does not occur, which heavily increases the subsequent treatment costs for the patients.
AIMS:
This research aims to evaluate cognitive features related to dementia progression based on neuropsychological tests’ data that are related to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) We utilise data related to two neuropsychological tests including the Clinical Dementia Rating Scale Sum of Boxes (CDR-SOB), and Mini-Mental State Examination (MMSE), to assess advancement of the AD.
METHODS:
To achieve the aim, we develop a data process called Neuropsychological Feature Assessment via Feature Selection (NFAFS) to identify impactful features using Information Gain (IG) and Pearson Correlation to assess class-feature and feature-feature correlations Later we will model a minimal subset of neuropsychological features using machine learning techniques to derive classification models.
RESULTS AND IMPLICATIONS:
Results obtained show key cognitive features of the MMSE are Time Orientation, Recall and Complex Attention, since they correlate with the progression class being ranked high in results of the feature selection techniques. For the CDR-SOB features, and aside from the memory feature it was difficult to identify other specific features that are signs of the dementia progression Clinicians can use specific features in a digital knowledge base to pay more attention to specific cognitive deficits related to Recall, Orientation and Complex Attention during dementia clinical evaluations in order to seek possible signs of the disease progression early.
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