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Privacy-Preserving Predictive Analytics for Healthcare Risk Stratification

Author(s) Sai Kalyani Rachapalli
Country United States
Abstract With the digitalization of healthcare systems and the adoption of Electronic Health Records (EHR), healthcare providers now have access to vast amounts of patient data that can be utilized for predictive analytics. Predictive modelling, particularly for risk stratification, has emerged as a vital tool in identifying high-risk patients and optimizing care delivery. However, the sensitive nature of health data poses significant privacy challenges, especially when data sharing is required across organizations. This paper explores privacy-preserving approaches for predictive analytics in the context of healthcare risk stratification. We provide a comprehensive methodology incorporating federated learning and homomorphic encryption to ensure data privacy while enabling high-performing predictive models. The study evaluates the effectiveness of these methods using real-world healthcare datasets, comparing privacy-preserving models with traditional machine learning techniques. Our results demonstrate that it is possible to achieve a balance between privacy and predictive performance, offering insights for secure and efficient deployment in healthcare environments. Further, we discuss implementation considerations, model optimization techniques, and ethical aspects of deploying such systems. Our findings indicate that privacy-preserving technologies can be seamlessly integrated into modern healthcare infrastructures to address data sharing limitations while ensuring high-quality patient care.
Field Engineering
Published In Volume 1, Issue 1, September 2020
Published On 2020-09-05
Cite This Privacy-Preserving Predictive Analytics for Healthcare Risk Stratification - Sai Kalyani Rachapalli - IJLRP Volume 1, Issue 1, September 2020. DOI 10.70528/IJLRP.v1.i1.1540
DOI https://doi.org/10.70528/IJLRP.v1.i1.1540
Short DOI https://doi.org/g9hncn
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