The increasing demand for health care services combined with the increasing costs of health care has been a major problem for healthcare systems all over the world. Predictive analytics which uses data science methodologies and machine learning models seems to be a possible way to address these issues. This paper explores the use of predictive analytics in controlling the costs of healthcare and improving the performance of operations in the healthcare sector. Using historical data, patient demographics, clinical outcomes, and real-time health monitoring, predictive models can predict patient needs, manage resources, and determine the most effective strategies to intervene. Case studies and practical applications are reviewed to determine how predictive analytics can assist in the identification of patients at risk, increase the effectiveness of hospital administration, and lead to the development of better treatment plans. Furthermore, it describes how predictive analytics can help in decision making, improve patient health and provide better care. The study also finds that predictive analytics enhances the performance of healthcare operations and reduces the overall costs of care without compromising on the quality of service. These technologies can help healthcare organizations better manage their resources, reduce costs, and provide proactive care, thus contributing to the long-term sustainability of the healthcare system. This article is written to highlight the potential benefits and applications of predictive analytics in healthcare, specifically addressing the pressing challenges of rising healthcare costs and increasing demand for services. It aims to provide an in-depth exploration of how predictive analytics—through the use of data science methodologies and machine learning models—can:
PYTHON, ML, DEEP LEARNING, Scikit-Learn, Linear Regression, Random Fores
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