A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data

Abstract

Alzheimer’s Disease (AD) is a neurodegenerative condition affecting predominantly elderly individuals, repre- senting the most common cause of dementia. Early clinical manifestations of AD include selective memory impairment, and while certain symptomatic improvements can be achieved through treatment, there is currently no cure. Magnetic Resonance Imaging (MRI) is utilized for brain imaging to assess suspected AD patients, providing results that include local and global brain atrophy. Some studies suggest that MRI features can predict the rate of AD decline and guide future treatments. However, to reach this stage, clinicians and researchers must employ machine learning techniques for accurate prediction of the progression from mild cognitive impairment to dementia. We propose the development of a reliable model to assist clinicians in achieving this and predicting early-stage Alzheimer’s Disease.