Published Paper Details:

Alzheimer "disease is a debilitating condition that results in the gradual destruction of brain cells, ultimately leading to dementia and impeding the affected individuals from carrying out their daily activities. While the treatment of this disorder is still in its nascent stages, early intervention and diagnosis hold promise for slowing down its progression. The utilization of magnetic resonance imaging (MRI) of the brain has emerged as a potential tool for the early detection of Alzheimer disease. To accomplish this, it is imperative to develop automated systems that can not only identify individuals with Alzheimer but also distinguish between the four distinct phases of the disease. The objective is to provide insights into the future of Alzheimer disease research, specifically in the context of stage prediction. The discussion centers around the application of various Machine Learning and Deep Learning methodologies and the advantages they offer in tackling this complex challenge. As part of this exploration, the article also delves into the limitations associated with deep learning techniques. Furthermore, several machine learning models are critically assessed to ascertain which approach holds the most promise for effectively addressing the multifaceted problem of Alzheimer disease diagnosis and stage prediction. This holistic approach aims to contribute to the development of more accurate, efficient, and reliable tools for the early identification and classification of Alzheimer disease, ultimately advancing our ability to combat this devastating" condition."