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Orchestrating Precision in Alzheimer's Disease Progression Forecasting: A Convergence of XGBoost and Random Forest Ensemble
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Author(s): G. S. G. Gunanidhi (Hindustan Institute of Technology and Science, India)and P. Selvi Rajendiran (HITS, 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.ch008
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Abstract
This study introduces a pioneering approach to forecast Alzheimer's disease progression, blending ensemble learning methods with the harmonious synergy of XGBoost and Random Forest algorithms. Through this fusion, our ensemble model orchestrates a symphony of predictive accuracy and reliability, harnessing the boosting capabilities of XGBoost and the robust tree aggregation of Random Forest. By integrating diverse datasets and employing advanced machine learning methodologies, our research aims to provide clinicians and caregivers with nuanced insights into individualized disease trajectories. This enhanced predictive modeling facilitates personalized care and intervention planning, optimizing patient outcomes and resource allocation.
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