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Review Papers | Computer Science and Information Technology | United States of America | Volume 14 Issue 1, January 2025 | Popularity: 3.9 / 10
Machine Learning Algorithms for Advanced Risk Stratification and Personalized Intervention Planning in Long-Term Care: A Focus on Gradient Boosting Machine (GBM) Algorithm
Bhanu Prakash Manjappasetty Masagali
Abstract: The global population is aging rapidly, leading to increased demands on long-term care (LTC) systems. Effectively managing elderly individuals with multiple health conditions and varying care needs is a significant challenge. Traditional risk stratification methods in LTC often fail to incorporate complex, evolving factors that could predict patient outcomes. Machine learning (ML) algorithms, notably the Gradient Boosting Machine (GBM), offer a robust, data-driven approach to improve risk stratification, identify at-risk individuals, and plan personalized interventions. This white paper explores how GBM can be leveraged to enhance LTC by providing accurate predictions, optimizing care delivery, and improving patient outcomes.
Keywords: Gradient Boosting Machine (GBM), risk stratification, intervention planning, long-term care, Extreme Gradient Boosting (XGBoost), preventive care
Edition: Volume 14 Issue 1, January 2025
Pages: 219 - 223
DOI: https://www.doi.org/10.21275/SR25103124013
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