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.
Highlights
Dementia diseases are defined as neuropsychiatric disorders and are among the most significant problems of old age
Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database
The primary goal of ADNI has been to test whether serial Magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD)
Summary
Dementia diseases are defined as neuropsychiatric disorders and are among the most significant problems of old age. Studies examining the sub-feature sets through blind search become essential, to facilitate the clinical diagnosis of doctors, and to make the health status prediction of an unlabeled sample more successful Such researches in this field may be described as much more helpful in order to give perspective to other studies to be conducted. In this experimental research, it is aimed to decrease the feature-count of the dataset as a first step to determine the most valuable brain characteristics that distinguish dementia diseases from each other. The conclusion is drawn, and future work is planned
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