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Bridging the Gap: Integrating Machine Learning With Biomarkers for Enhanced Alzheimer's Detection and Tracking
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Author(s): R. Ravi (J.J. College of Engineering and Technology), T. P. Sridevi (Aurora's PG College (MBA), India), N. Nirmala Devi (Aurora's Technological and Research Institute, India)and Sowmya Mandadi (Aurora's Technological and Research Institute, India)
Copyright: 2025
Pages: 26
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.ch001
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Abstract
Alzheimer's Disease (AD) is a relentless neurodegenerative disorder that profoundly affects cognitive abilities. Early detection and precise tracking of AD progression are pivotal for effective intervention and management. In this study, we introduce NeuroTrackNet, an innovative machine learning (ML) algorithm that seamlessly integrates a spectrum of biomarkers to enhance the detection and monitoring of Alzheimer's Disease. By leveraging a synergistic combination of imaging, genetic, and biochemical data, NeuroTrackNet significantly elevates diagnostic accuracy and offers robust tracking of disease progression. Our comprehensive validation on a robust dataset revealed NeuroTrackNet's impressive performance, achieving an overall accuracy of 92%, sensitivity of 90%, and specificity of 94%.
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