<![CDATA[Predictive analytics in healthcare has gained significant attention due to its ability to enhance decision-making, reduce hospital readmission rates, and improve patient outcomes. Machine learning (ML) plays a pivotal role in developing predictive models that analyze vast amounts of patient data to forecast health outcomes. This paper explores the application of ML techniques in healthcare predictive analytics, discusses commonly used algorithms, evaluates their effectiveness, and highlights challenges and future research directions. The integration of machine learning (ML) in predictive analytics enables the processing and analysis of vast amounts of patient data to identify patterns and predict health outcomes. This paper explores the application of ML techniques in healthcare predictive analytics, discusses commonly used algorithms, evaluates their effectiveness, and highlights challenges and future research directions. We present a case study using supervised learning models to predict patient readmission rates and compare their accuracy based on real-world healthcare datasets. The findings indicate that ML-driven predictive analytics can significantly enhance healthcare efficiency, reduce costs, and improve patient care through early intervention and risk mitigation strategies.]]>
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