A HYBRID MACHINE LEARNING TECHNIQUE FOR EARLY PREDICTION OF ALZHEIMER’S DISEASE

Abstract

In this paper, a new hybrid machine learning technique is proposed to detect the Alzheimer’s disease at a very early stage from MRI images, so that the neurodegenerative disease can be controlled and thereby preventing its progression at a very early stage. In this technique, the feature extraction is carried out using SURF, FAST, BRISK, Harris, Min Eigen and HOG methods and feature selection is carried out using Principal Component Analysis and fisher methods for early prediction of various stages of Alzheimer's disease. An analysis of the proposed method is done by combining it with Support Vector Machine, k Nearest Neighbor and Naive Bayes classifiers and performance parameters are evaluated. From the experimental results, the proposed model yields high accuracy rate and is found to be superior than the methods which uses single feature extraction and feature selection methods which are developed for the prediction and classification of Alzheimer’s disease.