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Advancing Alzheimer's Disease Detection With Big Data and Machine Learning
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Author(s): S. Mahesh (Aurora's Technological and Research Institute, India)and Rao K. Ram Mohan (Vasavi College of Engineering, India)
Copyright: 2025
Pages: 24
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.ch010
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Abstract
Alzheimer's disease (AD) detection and diagnosis face challenges due to its complexity. This study explores the fusion of advanced machine learning algorithms and big data methods to improve detection accuracy. In addition to commonly used algorithms like Random Forest and Support Vector Machines, the study introduces Gradient Boosting Decision Trees (GBDT) for AD prediction. GBDT combines the strength of multiple weak learners to enhance predictive performance. Furthermore, the study implements big data techniques such as data parallelization and distributed computing to handle large-scale datasets efficiently. By leveraging these methods, the study achieves a significant improvement in computational efficiency, enabling timely analysis of extensive AD-related data. Results show that the GBDT algorithm outperforms traditional methods, achieving an accuracy of 85% in predicting AD onset and progression. When combined with big data techniques, the overall accuracy further increases to 88%.
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