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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. 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