FILTER FEATURE SELECTION ANALYSIS TO DETERMINE THE CHARACTERISTICS OF DEMENTIA
Abstract
Dementias are known as neuropsychiatric disorders. As getting old, the chance of coming down with a dementia disease increases. Two-dimensional sliced brain scans can be generated via magnetic resonance imaging. Three-dimensional measurements of regions can be reached from those scans. For the samples in the ADNI dataset, the brain features are extracted through operating the Freesurfer brain analyzing tool. Parametrizing those features and demographic information in learning algorithms can label an unknown sample as healthy or dementia. On the other hand, some of the features in the initial set may be less practical than others. In this research, the aim is to decrease the feature-size, not the feature-dimension, as a first step to determine the most distinctive dementia characteristics. To that end, a total of 2264 samples (471 AD, 428 lMCI, 669 eMCI, 696 healthy controls) are divided into two sets: 65% training set (1464 samples) and 35% test set (800 samples). Various filter feature selection algorithms are tested over different parameters together with multiple Bayesian-based and tree-based classifiers. Test performance accuracy rates up to 76.50% are analyzed in detail. Instead of processing the whole feature set, the overall performance tends to increase with correctly fewer attributes taken.
Keywords
Alzheimer’s disease, Dementia, Filter feature selection, ADNI, Freesurfer
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