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Abstract
The integration of Artificial Intelligence (AI) into healthcare— particularly through predictive analytics—holds significant potential to transform care delivery from reactive treatment to proactive management. Despite this promise, effective implementation remains constrained by fragmented healthcare data, interoperability limitations, data quality challenges, and insufficient integration into clinical workflows. Most existing healthcare systems continue to operate reactively, emphasizing acute care rather than preventive strategies, thereby limiting the effectiveness of chronic disease management. AI-powered predictive analytics addresses these limitations by leveraging historical and real-time clinical data to anticipate disease progression, identify high-risk patient populations, and support timely, evidence-based interventions. Such capabilities can reduce healthcare costs, minimize emergency department visits, and lower hospital readmission rates. However, widespread adoption is impeded by infrastructural constraints, clinician resistance to new technologies, and concerns related to model transparency, trust, and explainability. To overcome these challenges, the proposed system employs a modular, service oriented architecture with support for hybrid deployment environments. The architecture integrates key components, including data ingestion, preprocessing, feature engineering, model training, and predictive services, alongside intuitive dashboards for clinicians and administrators. Functional requirements encompass patient risk stratification and operational forecasting, while non-functional requirements prioritize system performance, scalability, reliability, security, and regulatory compliance. Stakeholder analysis highlights the needs of clinicians, administrators, and other healthcare participants, emphasizing usability, seamless integration with existing systems, and transparent decision-support mechanisms.