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Abstract
The healthcare sector is undergoing a rapid digital transformation driven by the increasing adoption of electronic health records, wearable health monitoring devices, medical imaging systems, telemedicine platforms, and advanced diagnostic technologies. These systems generate enormous volumes of clinical, operational, and patient-centered data on a daily basis. However, despite the availability of such large-scale healthcare data, many healthcare institutions continue to face significant challenges in transforming raw data into actionable insights for timely and effective decision-making. Early identification of patient risk remains a critical concern, particularly in cases involving chronic diseases, acute clinical deterioration, hospital readmissions, adverse drug reactions, and emergency care interventions. Intelligent data analytics models have emerged as powerful tools for addressing these challenges by enabling early risk prediction and supporting proactive healthcare management. By integrating artificial intelligence, machine learning, predictive analytics, and data engineering techniques, healthcare organizations can identify hidden patterns, correlations, and trends within large and heterogeneous datasets. These insights facilitate timely interventions, improve patient safety, reduce healthcare costs, and enhance overall operational efficiency. This study proposes an intelligent data analytics framework for early risk prediction in healthcare systems. The proposed framework utilizes advanced predictive modeling techniques to analyze clinical and operational data in real time and generate risk predictions that support healthcare professionals in evidence-based decision-making. Unlike conventional rule-based or retrospective analytical systems, the proposed model emphasizes real-time data processing, predictive intelligence, scalability, and interpretability. The research also examines critical challenges affecting the adoption of intelligent analytics in healthcare, including fragmented data sources, poor data quality, interoperability limitations, security concerns, privacy issues, and resistance to AI adoption in clinical practice. A detailed stakeholder analysis is conducted to understand the requirements and expectations of clinicians, patients, administrators, IT professionals, and analytics teams. Functional and non functional requirements are identified to ensure that the system is practical, scalable, secure, and suitable for real-world healthcare environments. The results demonstrate that intelligent data analytics models significantly improve early risk detection, support clinical decision-making, optimize resource allocation, and contribute to better patient outcomes. The proposed framework has strong potential to serve as a next-generation healthcare SnehaRagaSowjanya Midde., et al / Int. J. of Intel Adv Res in Eng Compts. Vol-14(1) 2026 [10-23] intelligence platform capable of improving the quality, efficiency, and sustainability of healthcare delivery systems.