An Explainable Ensemble Machine Learning Framework for Early Detection of Alzheimer’s Disease Using Clinical Data

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia, accounting for about 60–80% of cases worldwide. It is characterised by a gradual decline in cognitive functions such as memory, thinking, orientation, comprehension, and communication. The underlying pathology involves the buildup of beta-amyloid plaques and tau protein tangles in the brain, which cause neuronal damage and brain shrinkage over time. Clinically, symptoms range from mild forgetfulness to severe cognitive impairment, eventually impairing daily functioning. Globally, over 55 million people have dementia, with Alzheimer’s being the primary contributor. The societal and economic impacts are enormous, with dementia-related costs exceeding $1 trillion annually. A major challenge is that AD is often diagnosed only in later stages, after extensive neurodegeneration has occurred. Early detection is essential to slow progression, enhance quality of life, and optimise treatment options. Keywords – Alzheimer’s Disease (AD), Machine Learning (ML), Early Detection, Explainability, Ensemble Learning, Clinical Data, Diagnostic Framework.