Diagnosis of Alzheimer's Disease using Pearson's Correlation and ReliefF Feature Selection Approach

The study of brain connectivity patterns reveal important information in the understanding of the brain's functional organization. Resting-state functional magnetic resonance imaging (rs-fMRI) is a type of neuroimaging technique that can be used to diagnose a variety of neurological conditions. In this study, Pearson's correlation connectivity (PCC) and the feature selection algorithm ReliefF are used to distinguish Alzheimer's disease (AD) patients from normal controls (NC). PCC is a common measure to find the correlation between regions and ReliefF is known to perform well with high dimensional feature vectors so the combination of two gives a good accuracy. Using a k-nearest neighbor (KNN) classifier, the proposed method achieved a classification accuracy of 93.5 percent, showing the good potential of the proposed approach.