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Abstract

This review paper explores the role of data analytics in reducing healthcare disparities by examining various predictive models and their applications in promoting health equity. It provides a historical context of healthcare disparities and traces the evolution of predictive models from traditional statistical methods to advanced machine learning algorithms. Key studies highlighting the effectiveness and limitations of these models in addressing health inequities are discussed, along with the ethical considerations and biases inherent in data analytics. The paper also identifies future opportunities for enhancing predictive models, emphasizing the integration of social determinants of health (SDOH) to improve accuracy and relevance. Policy implications and recommendations for future research and development are provided, aiming to leverage predictive analytics to create a more equitable healthcare system.
Keywords:  Healthcare Disparities, Predictive Models, Health Equity, Data Analytics, Social Determinants of Health