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

Authors: Murat Seckin Ayhan; Ryan G. Benton; Vijay V. Raghavan; Suresh Choubey

Addresses: Center for Advanced Computer Studies, University of Louisiana at Lafayette, 301 E. Lewis St., 201-G Oliver Hall (ACTR), Lafayette, LA 70503, USA ' Center for Advanced Computer Studies, University of Louisiana at Lafayette, 301 E. Lewis St., 201-G Oliver Hall (ACTR), Lafayette, LA 70503, USA ' Center for Advanced Computer Studies, University of Louisiana at Lafayette, 301 E. Lewis St., 201-G Oliver Hall (ACTR), Lafayette, LA 70503, USA ' Quality Operations, GE Healthcare, 3000 Grandview Blvd., Waukesha, WI 53018, USA

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 approaches that use a combination of clinical assessments. In this study, we compare Naïve 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.

Keywords: Alzheimer's disease; stereotactic surface projection; 3D SSP; naïve Bayes; SVM; support vector machines; correlation-based feature selection; predictive modelling; dementia; positron emission tomography; PET scans; feature extraction.

DOI: 10.1504/IJDMB.2013.053194

International Journal of Data Mining and Bioinformatics, 2013 Vol.7 No.2, pp.146 - 165

Received: 29 Apr 2011
Accepted: 29 Apr 2011
Published online: 20 Oct 2014 *

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