Keywords
biomarkers
early detection
multimodal data
How to Cite
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Early detection of AD biomarkers is crucial for timely diagnosis and intervention. This paper proposes machine learning models for the early detection of AD biomarkers from multimodal data sources. The models leverage various data modalities, including imaging, genetic, and clinical data, to identify patterns indicative of AD onset. We present a comprehensive review of existing literature on AD biomarkers and machine learning approaches. Our proposed models integrate data from different sources to enhance predictive accuracy and reliability. We evaluate the performance of the models using real-world datasets and demonstrate their potential for early detection of AD biomarkers.
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