Machine Learning Framework for Stagewise Classification of Alzheimer’s Disease

Abstract

Alois Alzheimer, a German physician, diagnosed the first case of Alzheimer’s disease (AD) in the early twentieth century, so it is named after him. The patient experienced loss of memory, depression, and psychological changes. In the autopsy, Alzheimer noticed that the nerve cells in and around her brain were weakening. It is caused medically by abnormal protein build up in and around the brain cells. Amyloid and tau are two proteins that are involved. Few causes of this disease are: age, family history, head injury, etc. AD is one of the different varieties of dementia. It usually affects persons over the age of 60 but now it affects adults in their middle years as well. As a result, experts are focusing on this disease and using various study strategies to try and control it. Initially, they focused on discovering a treatment for this illness, but as time went on, their attention shifted to disease analysis and prediction. For efficient treatment and recovery of AD, precise early-stage identification is important. As a result, accurate AD diagnosis is a significant research challenge. The dataset used in this study consists of 416 medical records. To provide accurate results, a machine learning (ML) algorithm is used to construct the model. In this study, we provide users a stage by stage prediction of Alzheimer’s disease. The stages included are: non-demented, mild demented, moderate demented, and demented. It is a challenging disease for which there is no cure; instead, we can only delay the progression of the disease.