Exploring the methods on early detection of Alzheimer's disease

Abstract

Alzheimer’s disease (AD) is a disorder which is said to be irreversible and affects the behavior and cognitive processes which will eventually affect the memory. This disease beget difficulty in performing the daily task of a patient. It is one of the most common form of dementia affecting people above the age 65 and the risk increases with age. The treatments currently available can only mitigate AD progression but there is no treatment to stop this progression. To bring down the progression of AD early detection becomes necessary. Researchers have found that many machine learning (ML) methods have been useful in detection of AD. Machine learning is a part of artificial intelligence involving probabilistic and optimization techniques such as neural networks that prepares pc's to gain a model from complex datasets. This paper Scrutinizes the developments taken in the field of ML for the possibly early diagnosis of AD. It discusses about various approaches used in recent times for the detection of AD at an early stage. Through this research we found several classification methods such as Recurrent neural networks(RNN), Convolution neural networks(CNN), many more binary and multiclass classifiers along with various methods of preprocessing steps involved in the detection of AD. This paper also throws light on the datasets being used and how these preprocessing steps and different classifiers attribute to increase of accuracy in prediction of AD. Finally, coming to the objective of this paper is to analyze and evaluate these different techniques of ML contributing for the detection AD as early as possible and also to help the researchers to get maximum information and comparison of techniques in one go.