An Early Prediction and Detection of Alzheimer's Disease: A Comparative Analysis on Various Assistive Technologies

Alzheimer's disease (AD) is a leading form of Dementia which has recently gained a large attention in neuroimaging techniques. The symptoms are very slow and it affects the daily routine of a human being. AD is not an old age disease; it also affects people of different age. The early stage of the disease is a mild memory loss followed by degradation in the conversation and communication of a patient. The current treatments have no solution to stop the disease but early diagnosis will reduce the severity of the disease and help the patients to live a quality life. Research says that the count of individuals affected with AD will duple in next 20 upcoming years. In this paper, a systematic review on Dementia leading to Alzheimer's disease is performed using various approaches for diagnosis of AD. Various analysis and evaluation techniques performed on recent work for the early detection of AD using various approaches of machine learning, IOT, Artificial Intelligence, etc is also reviewed. This paper also discusses about the future research directions and challenges in handling Alzheimer's data. Though, the analysis on techniques produces a promising prediction, the evaluations are done only for a variety of pathologically unproven data sets. Different imaging modalities are also applied which cannot be evaluated to make a fair comparison among them.