Improved reasoning with uncertainty based significant feature subset selection for alzheimer’s disease detection

Abstract

In this modern era neurodegenerative disorder of undefined causes affects the older adults and it becomes most cause of dementia. The Alzheimer’s disease is one of such neurodegenerative disorder which is very complex and hard to predict in the early stage. With evolving advancement in the field of machine learning, it is possible to predict the early stage of AD and diagnosing in initial stages may produce effect result for their further quality and healthy life. But uncertainty in determination of Alzheimer’s is a toughest challenge for the researchers in the field of machine learning. This paper aims to overcome the uncertainty in discovering dementia and non-dementia victims of Alzheimer’s by devising an improved reasoning with uncertainty based prominent feature subset selection using modified fuzzy dempster shafer theory (IRU-DST). For Alzheimer’s disease prediction the dataset is used form OASIS dataset. The performance of the proposed IRU-DST is validated using fuzzy artificial neural network. The simulation results proved the performance of the IRU-DST achieves better results comparing the other sate of arts, by gaining high accuracy rate and it also minimize the error rate considerably with the ability of handling uncertainty.