AI-Driven Cloud Architecture for Secure and Scalable Environmental Health Systems: Integrating SAP, Open Environmental APIs, and Governance Frameworks for Cancer Outcome Analytics
DOI:
https://doi.org/10.15662/IJARCST.2025.0806804Keywords:
AI-driven cloud architecture, environmental health informatics, SAP integration, Open Environmental APIs, cancer outcome analytics, federated learning, risk-aware framework, environmental pollutants, predictive modeling, cloud security, digital health transformation, sustainable healthcare systems.Abstract
The intersection of environmental data, enterprise analytics, and health informatics offers new opportunities to understand and mitigate cancer risks associated with environmental pollutants. This paper proposes an AI-driven cloud architecture designed to enable secure, scalable, and interoperable integration of environmental, clinical, and enterprise data sources. The architecture leverages SAP enterprise systems and Open Environmental APIs to unify heterogeneous datasets—such as air and water quality indices, industrial emissions, and patient health outcomes—into a shared analytics ecosystem. Through machine learning orchestration, predictive modeling, and automated data governance, the framework supports dynamic risk assessment, real-time anomaly detection, and evidence-based decision-making in environmental health research. A risk-aware governance framework ensures compliance with global data privacy standards, while federated learning enables multi-institutional collaboration without exposing sensitive data. The results demonstrate how the proposed architecture enhances the scalability, transparency, and analytic depth of cancer outcome studies, bridging the gap between environmental informatics and precision public health.
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