Data fusion and feature selection for Alzheimer's diagnosis

Abstract

The exact cause of Alzheimer's disease is unknown; thus, ascertaining what information is vital for the purpose of diagnosis, whether human or automated, is difficult. When conducting a diagnosis, one approach is to collect as much potentially relevant information as possible in the hopes of capturing the important information; this is the Alzheimer's Disease Neuroimaging Initiative (ADNI) adopted approach. ADNI collects different clinical, image-based and genetic information related to Alzheimer's disease. This study proposes a methodology for using ADNI's data. First, a series of support vector machines is constructed upon nine data sets. Five are the results of clinical tests and the other four are features derived from positron emission tomography (PET) imagery. Next, the SVMs are fused together to determine the final clinical dementia rating of a patient: normal or abnormal. In addition, the utility of applying feature selection methods to the generated PET feature data is demonstrated. © 2010 Springer-Verlag.