Secure AI-Cloud Ecosystem for Healthcare Data Analytics: Integrating SVM and SAP Intelligence
DOI:
https://doi.org/10.15662/IJARCST.2025.0805020Keywords:
AI-Cloud Ecosystem, Healthcare Data Analytics, Support Vector Machine (SVM), SAP Intelligence, Data Security, Federated Learning, Cloud-Native Architecture, Predictive HealthcareAbstract
This paper introduces a Secure AI-Cloud Ecosystem for Healthcare Data Analytics that integrates Support Vector Machine (SVM)-based intelligence and SAP-driven data management to enhance security, scalability, and analytical precision in healthcare operations. The proposed framework leverages cloud-native infrastructure to ensure real-time data processing, interoperability, and resilience across distributed healthcare environments. AI algorithms embedded with SVM optimize predictive diagnostics, patient outcome modeling, and anomaly detection, while SAP Intelligence enables seamless data governance, workflow automation, and compliance with healthcare regulations such as HIPAA and GDPR. The security layer integrates encryption, role-based access, and federated learning to preserve data privacy without compromising analytical depth. This unified architecture fosters transparency, efficiency, and trust across multi-institutional healthcare networks, paving the way for intelligent, privacy-aware, and adaptive digital healthcare ecosystems.
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