AI-Powered Healthcare Security Intelligence: Machine Learning Federated Learning Pipelines and Explainable Analytics on AWS
DOI:
https://doi.org/10.15662/IJEETR.2023.0504003Keywords:
AI-powered healthcare, Security intelligence, Machine learning, Federated learning pipelines, Explainable AI, AWS cloud, Fraud prevention, Regulatory complianceAbstract
The increasing volume and sensitivity of healthcare data require advanced approaches to ensure security, fraud prevention, and regulatory compliance. This paper presents an AI-powered framework for healthcare security intelligence that integrates machine learning, federated learning pipelines, and explainable analytics on AWS cloud infrastructure. By leveraging federated learning, the system enables collaborative model training across distributed healthcare institutions without sharing sensitive patient data, ensuring privacy and compliance. Explainable AI techniques enhance transparency and interpretability of predictive models, aiding clinicians and administrators in understanding risk patterns and potential security threats. The proposed architecture supports real-time anomaly detection, fraud prevention, and operational reliability, demonstrating scalability and robustness in cloud-native healthcare environments. Future directions include enhancing model generalization across diverse healthcare datasets, integrating ethical AI principles, and optimizing resource allocation for large-scale federated learning deployments.
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