CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORK

Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing attention of neuroscientists and computer scientists, since it opens a new window to explore functional network of human brain with relatively high resolution. BOLD technique provides almost accurate state of brain. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyse the relationship among damaged part. By computational method especially machine learning technique we can show such classifications. In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal patient’s dataset. After proper processing the fMRI data we use the processed data to form classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve Bayes. We also compare the accuracy of our proposed method with existing methods. In future, we will other combinations of methods for better accurac