Alzheimer's Disease Detection using Ensemble Approach

Alzheimer's disease (AD) is a neurodegenerative, irrevocable, major brain dysfunction and complicated disease that continuously affects synapses, leading to memory and concentration skills degradation and, consequently, the inability to do even the most fundamental activities. This disease causes cognitive loss, which eventually results in dementia. AD causes cell death and damage to tissues throughout the brain, significantly reducing brain volume over time and impacting most of its activities. This study looks in depth at effective and consistent learning methodology for Alzheimer's disease classification. K-Nearest Neighbor (KNN), Convolutional Neural Networks (CNN), and Ensembled Approach (EA) are the key machine learning methodologies investigated in this study. The ensembled approach outperforms the other approaches for diagnosing neurological disease. When compared to the state-of-the-art, the obtained results have demonstrated superior performance in terms of classification accuracy of 97.3%.