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

How to Cite?: Bhanu Prakash Manjappasetty Masagali, "Machine Learning Algorithms for Advanced Risk Stratification and Personalized Intervention Planning in Long-Term Care: A Focus on Gradient Boosting Machine (GBM) Algorithm", Volume 14 Issue 1, January 2025, International Journal of Science and Research (IJSR), Pages: 219-223, https://www.ijsr.net/getabstract.php?paperid=SR25103124013, DOI: https://dx.doi.org/10.21275/SR25103124013