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Integrating Machine Learning in Biological Markers for Enhanced Early Detection of Alzheimer's Disease
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Author(s): B. Sriman (Rajalakshmi Institute of Technology, India), R. Seetha (Vellore Institute of Technology, Chennai, India), A. Durga Devi (P.B. College of Engineering, India), S. Renuka Devi (Rajalakshmi Engineering College, India)and R. S. Rashika (Panimalar Engineering College, India)
Copyright: 2025
Pages: 18
Source title:
Deep Generative Models for Integrative Analysis of Alzheimer's Biomarkers
Source Author(s)/Editor(s): Abhishek Kumar (Chandigarh University, India), S. Rakesh Kumar (GITAM University (Deemed), India), N. Gayathri (GITAM University (Deemed), India), R. Srivel (Adhiparasakthi Engineering College, India)and Dhaya C. (Adhiparasakthi Engineering College, India)
DOI: 10.4018/979-8-3693-6442-0.ch005
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Abstract
Alzheimer's disease (AD) presents a significant challenge in healthcare due to its progressive nature and the absence of definitive early detection methods. Recent advancements in machine learning (ML) have shown promise in integrating with biological markers to improve the early detection of AD. This paper explores the synergistic potential of ML algorithms with various biological markers, such as genetic factors, biomarkers in cerebrospinal fluid, and neuroimaging data. The integration aims to enhance prediction accuracy and reliability in identifying individuals at high risk of developing AD before clinical symptoms manifest. Challenges including data heterogeneity, scalability of models, and ethical considerations are also discussed. By leveraging ML techniques alongside biological markers, this approach holds potential to revolutionize early detection strategies for Alzheimer's disease, ultimately facilitating timely interventions and improving patient outcomes.
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