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Abstract: This paper delves into the core algorithms and techniques employed in healthcare predictive analytics, including machine learning, statistical modeling, and data mining. We explore the multifaceted applications of this technology, encompassing improved patient stratification for risk assessment, targeted interventions for disease prevention, and optimized resource allocation for healthcare systems. However, the implementation of predictive analytics necessitates careful consideration of ethical issues surrounding data privacy and potential biases within algorithms. Regulatory frameworks may also require adaptation to ensure responsible use of this technology, this research emphasizes the transformative potential of predictive analytics in healthcare, paving the way for a future of proactive medicine and personalized care.
Ayas Ahmad (2024) studied this question.
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