Exploitation of 3D stereotactic surface projection for predictive modelling of Alzheimer's disease

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 approaches that use a combination of clinical assessments. In this study, we compare Naive Bayes (NB) with variations of Support Vector Machines (SVMs) for the automatic diagnosis of AD. 3D Stereotactic Surface Projection (3D-SSP) is utilised to extract features from PET scans. At the most detailed level, the dimensionality of the feature space is very high. Hence we evaluate the benefits of a correlation-based feature selection method to find a small number of highly relevant features; we also provide an analysis of selected features, which is generally supportive of the literature. However, we have also encountered patterns that may be new and relevant to prediction of the progression of AD.