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
Affiliations:
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
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2013

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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.