Machine Learning–Enabled Cloud-Native SAP Optimization for Risk-Aware Healthcare and Enterprise Operations
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805023Keywords:
Machine Learning, Cloud-Native, SAP Systems, Healthcare Business Processes, Enterprise Operations, Risk Management, Database Auto-Tuning, Intelligent UI Performance, Scalable Systems, Agile ArchitectureAbstract
Healthcare and enterprise organizations increasingly rely on SAP systems to manage complex business processes, necessitating high performance, scalability, and robust risk management. This study presents a machine learning–enabled, cloud-native framework for SAP optimization that integrates database auto-tuning, intelligent user interface (UI) performance enhancement, and risk-aware analytics. Leveraging adaptive machine learning and self-supervised deep learning models, the framework continuously monitors system behavior, detects anomalies, and optimizes computational and operational resources. By operating within cloud-native architectures and agile deployment models, the approach ensures scalable, secure, and efficient system performance while proactively managing operational and compliance risks. Experimental evaluation demonstrates significant improvements in database query efficiency, UI responsiveness, and risk detection capabilities, highlighting the potential of machine learning–enabled cloud-native SAP optimization to transform healthcare and enterprise operations.
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