The Application of Machine Learning in Alzheimer's Disease

Abstract

While mentioning Alzheimer's disease, it’s horrible elderly characterized by the loss of important memories and fundamental cognitive disability. Currently, machine learning has been widely applied in the early analysis and recognition of Alzheimer's disease. This paper explores the predictive methods for Alzheimer's disease, including the application of machine learning techniques in this field. The paper compares the traditional MoCa assessment method with the machine learning-based approach, highlighting the latter's advantages in diagnostic accuracy and efficiency. It provides a detailed analysis of the main components of the machine learning predictive method, introducing their functions, advantages, and disadvantages in prediction. Subsequently, through multiple research case studies, the effectiveness and advantages of machine learning methods are demonstrated, showing superiority in both accuracy and universality. Finally, the paper emphasizes the exploration of the causation of Alzheimer's disease and proposes future directions for improvement in research, the importance of interdisciplinary collaboration, and prospects for future research.