A Stack Ensemble Approach for Early Alzheimer Classification Using Machine Learning Algorithms.

The incorporation of machine learning techniques in medical research has facilitated the exploration of novel avenues for the timely identification of diseases. The continuous progress in medical technology has facilitated the acquisition of complex and complete datasets, which in turn enhances the ability to identify medical diseases in their early stages. Alzheimer’s disease, a significant and hard problem, is characterised by the slow degeneration of brain cells and has a profound impact on cognitive functions, namely memory. It occupies a prominent position within this domain. In the middle of these exciting promises, there remains a significant research gap that pertains to the absence of thorough empirical evidence about the effectiveness of machine learning algorithms in the early identification of Alzheimer’s disease. The primary objective of this study is to address the existing research gap by conducting a comprehensive and meticulous series of experiments. A comprehensive examination of data obtained from sophisticated neuroimaging technologies is performed by utilising a wide range of machine learning models, such as Logistic Regression, Naive Bayes, Neural Networks, Random Forest, and the Stack ensemble. The primary objective is to facilitate the prompt detection of Alzheimer’s disease, hence enabling expedited interventions and therapeutic approaches. As one embarks on the journey of research, the unfolding narrative is shaped by the use of empirical evidence, establishing a strong foundation in the convergence of state-of-the-art technology and the urgent healthcare need to detect early stages of Alzheimer’s disease. Furthermore, this research not only addresses existing gaps in the literature but also ends in the identification of the most effective machine learning model, specifically the Neural Network, which has an accuracy rate of 87