SUPPORT VECTOR MACHINE BASED FRAMEWORK FOR DEMENTIA CLASSIFICATION

Aims: Dementia is fast rising as a huge public health problem in recent times due to its extreme prevalence rate, huge burdens to patients in terms of health care costs and so on. Identifying alterable risk elements is significant for delaying or even preventing the onset of dementia. Magnetic resonance Imaging (MRI) is an affordable as well as non-radioactive imaging technique which does not have ionizing radiations. It possesses excellent spatial resolution and is commonly accessible within clinical environments. In the current work, image extraction is carried out through usage of Gabor as well as Grey-Level Co-Occurrence Matrix (GLCM). Classification is carried out through classifiers like K-Nearest Neighbor (KNN), Classification and Regression Tree (CART) as well as Support Vector Machines (SVM).