Affiliations: [a] Department of Computer Science and Engineering, The NorthCap University, Gurugram, India | [b] Delhi Skill and Entrepreneurship University, New Delhi, India | [c] Department of Neurosurgery, Fortis Hospital, New Delhi, India | [d] Department of Neurosurgery, Max Super Speciality Hospital, New Delhi, India
Correspondence:
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Corresponding author: Deepika Bansal, Department of Computer Science and Engineering, The NorthCap University, Gurugram, India. E-mail: [email protected].
Abstract: Dementia is a brain condition that impairs the cognitive abilities of an individual. Mild cognitive impairment is a mediator phase of healthy and dementia controls. The motivation of this study is to predict dementia using magnetic resonance imaging data, which is significant for the diagnosis of normal control and dementia patients. The proposed model leverages effective methods like Discrete Wavelet Transform, Bag of Features, and Support Vector Machine. The four wavelets haar, Daubechies, symlets, and coiflets are used for image compression. The results of the proposed data intelligence model are promising in terms of accuracy which is 92.32% which is better than the recently proposed models. Also, the proposed data intelligence model is compared with the models which may use curvelet transform, and shearlet transform and with the methods which have gone without using DWT transforms. The comparisons have also been made with the models that have used other prevalent techniques like Principal Component Analysis, Fisher Discriminant Ratio, and Gray Level Co-occurrence Matrix. The outcomes support the usage of each technique in the suggested data intelligence paradigm.
Keywords: Data intelligence, dementia, magnetic resonance imaging, discrete wavelet transform, bag of features, support vector machine
