Machine Learning and Deep Learning Techniques for Alzheimer's Disease Prediction Using CSF and Plasma Biomarkers

Alzheimer's disease (AD) is a progressive neurodegenerative disease with vast worldwide consequences. Timely and accurate diagnosis is crucial but present methods are costly and resource-intensive. This research work investigates the use of CSF and plasma biomarkers, specifically amyloid beta (
$\mathrm{A}\beta$
) and tau, in conjunction with machine learning (ML) and deep learning (DL) algorithms to enhance AD prediction. The proposed method seeks to close the gap in accurate early detection and classification. The findings show that improved computational techniques hold the potential to enhance AD diagnosis by leveraging CSF and plasma biomarkers.