Prediction of Possible conversion from MCI to AD using Machine learning

Abstract

Alzheimer’s is an irreversible brain disease that impairs memory, thinking and behavior and leads ultimately to death. It is a major public health problem in the elder population and has a huge impact on society. It is useful to diagnose AD as early as possible, in order to improve the quality of life of the patient and their care takers. In this study we analyze the performance of different machine learning methods to predict the possible conversion from MCI to AD. We conducted many experiments with various learning algorithms and achieved performance levels comparable to the published results in this domain. The results are very promising and demonstrate the utility of machine learning methods in this domain.

Keywords: Machine learning, Alzheimer’s disease, Mild cognitive Impairment, Support vector machine, neural network, Feature subset selection