Secure AI-Driven Cloud-Native Healthcare Analytics Using Machine and Deep Learning with API-Centric Cybersecurity Architecture
DOI:
https://doi.org/10.15680/IJCTECE.2024.0703006Keywords:
Healthcare Analytics, Cloud-Native Architecture, Artificial Intelligence, Machine Learning, Deep Learning, API-Centric Security, Cybersecurity, MLOps, Microservices, Healthcare Data PrivacyAbstract
The increasing digitalization of healthcare systems has led to massive growth in heterogeneous and sensitive medical data, necessitating analytics platforms that are not only intelligent and scalable but also secure by design. This paper presents a secure AI-driven cloud-native healthcare analytics architecture that leverages machine learning and deep learning techniques for advanced clinical and operational intelligence while enforcing API-centric cybersecurity controls. The proposed architecture adopts cloud-native software engineering principles, including microservices, containerization, and orchestration, to enable elastic scaling, high availability, and fault tolerance. Machine learning and deep learning models are deployed through automated MLOps pipelines to support predictive analytics, medical image analysis, anomaly detection, and patient risk stratification in both real-time and batch processing scenarios. Security is embedded across the system lifecycle using secure API gateways, zero-trust access control, encrypted data exchange, and continuous monitoring to protect sensitive healthcare data and ensure regulatory compliance. Standardized APIs enable seamless interoperability between electronic health records, clinical decision support systems, and third-party healthcare services. Experimental analysis indicates improved analytics performance, reduced latency, and enhanced security posture compared to traditional monolithic healthcare platforms. The proposed framework serves as a practical reference architecture for building next-generation healthcare analytics systems that combine AI intelligence, cloud-native scalability, and robust cybersecurity.
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