A Survey of Different Machine Learning Models for Alzheimer Disease Prediction

Abstract

Machine learning model is one of the best disease prediction framework in various medical disease prediction processes. Alzheimer’s disease (AD) is a progressive neuro-degenerative condition with different severity features. However, it is noted that very few patients who is suffering from Alzheimer’s disease are decided to take correct clinical decision making. Most of the traditional machine learning models help to detect the AD with limited feature space and dimensionality. Also, these models are not applicable to high dimensional features due to sparsity problem. Several high dimensional classification and clustering methods have recently been proposed to predict the AD automatically. Component selection plays a significant role in improving the performance of these programs. Therefore, various forms of feature selection techniques are analyzed in this survey article. The purpose of the paper is to include an analytical overview and strategic examination of the latest research work performed using Machine Learning Strategies to early diagnosis of AD.