No abstract

Alzheimer’s Disease (AD) is significantly increasing for older adults in the world nowadays. It affects 10% of people over the age of 65 and 50% of people over 85 years of age. The majority of patients live at home and they are cared for by their family and friends. AD disease is an unavoidable neurological type of disorder, where brain cells die slowly and result in memory loss, leading to Dementia. Sometimes AD disease also affects people of lower age groups. There are three stages of Dementia: 1) Mild, 2) Moderate and 3) Severe. Mild and Moderate stages are treated to some point and in the third stage, there is no treatment for the people. Therefore an early diagnosis is required for this type of disease to avoid a significant loss. As a result, early detection and prediction of infection are needed. Apart from medical researchers, researchers from IoT, AI, Machine Learning have also identified various techniques by which the disease can be predicted and the patient can be monitored for their daily activity. In this paper, various routines to diagnose Alzheimer’s disease are analyzed. The interpretation and evaluation techniques available are also listed out to the early diagnosis of the disease. We have also highlighted early detection and prediction techniques and the promising methods are emphasized.