Machine learning approach for Alzheimer’s disease classification

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Alzheimer’s disease (AD) is a neurodegenerative disorder that affects memory, thinking, and behaviour over time. It is the most common cause of dementia in older adults1 and is characterised by the abnormal amyloid beta protein and tau protein accumulation in the brain. These protein abnormalities cause brain cell death and loss of brain function. AD symptoms typically begin with mild memory loss and difficulty in completing familiar tasks. As the disease progresses, symptoms such as difficulty in communicating, disorientation, mood, behaviorAA changes, and inability to care for oneself, may become more severe.

The machine learning (ML) techniques have been used in the field of AD to analyze various types of data for the identification of distinguishing patterns associated with the disease. Previously, ML have been utilized for the AD detection and diagnosis to identify individuals at risk of developing AD before the onset of significant symptoms This might be useful for the earlier intervention, potentially slowing disease progression. Similarly, ML has also been used to analyze genetic data, which may aid in identifying individuals who are more likely to develop AD, allowing for targeted prevention or early intervention strategies.