Keywords
biomarkers
How to Cite
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the elderly population. Early detection of AD biomarkers is crucial for timely diagnosis and intervention, yet remains a challenging task. This paper presents a comprehensive review of machine learning (ML) models developed for the early detection of AD biomarkers from multimodal data sources. We discuss the importance of early detection, challenges in biomarker identification, and the role of ML in improving diagnostic accuracy. We also provide an overview of existing datasets and evaluation metrics used in AD biomarker research. Additionally, we highlight the potential of ML models to enhance early detection and facilitate personalized treatment strategies for AD patients.
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