Alzheimer's Diagnosis

Abstract

Alzheimer's disease poses a serious threat to world health, affecting millions of people, due to its irreversible nature. Accurate diagnosis and early detection are essential. This book chapter analyses the OASIS longitudinal dataset and delivers thorough research using machine learning (ML) techniques. The goal of this research is to accurately forecast the development of dementia, offering a proactive strategy to address the rising incidence of Alzheimer's. Through the analysis of a variety of variables, such as clinical evaluations and socioeconomic status, we identify trends influencing the illness. The chapter covers preprocessing, eight machine learning methods, hybrid deep learning techniques and exploratory data analysis, emphasising the effectiveness of random forest and AdaBoost. Surprisingly, 98.5% accuracy is achieved in deep learning using artificial neural networks. This chapter provides a comprehensive knowledge, bridging the gap between early prediction and personalised intervention in Alzheimer's disease, culminating in a predictive dashboard.