Exploitation of 3D Stereotactic Surface Projection for automated classification of Alzheimer's disease according to dementia levels

Abstract

Alzheimer's disease (AD) is one major cause of dementia. Previous studies have indicated that the use of features derived from Positron Emission Tomography (PET) scans lead to more accurate and earlier diagnosis of AD, compared to the traditional approach used for determining dementia ratings, which uses a combination of clinical assessments such as memory tests. In this study, we compare Naïve Bayes (NB), a probabilistic learner, with variations of Support Vector Machines (SVMs), a geometric learner, for the automatic diagnosis of Alzheimer's disease. 3D Stereotactic Surface Projection (3D-SSP) is utilized to extract features from PET scans. At the most detailed level, the dimensionality of the feature space is very high, resulting in 15964 features. Since classifier performance can degrade in the presence of a high number of features, we evaluate the benefits of a correlation-based feature selection method to find a small number of highly relevant features.