Main Article Content
Abstract
Predictive analytics has become a pivotal tool in modern healthcare, enabling improvements in clinical decision-making, operational performance, and patient outcomes. By utilizing both historical and real-time healthcare data, predictive models can anticipate critical events such as disease progression, hospital readmissions, adverse drug reactions, and fluctuations in healthcare resource demand. This research presents the development of an intelligent predictive analytics framework aimed at enabling proactive healthcare management and effective decision support. In contrast to conventional statistical techniques, the proposed system employs advanced machine learning and deep learning approaches capable of identifying complex, non-linear relationships within large and heterogeneous datasets, thereby enhancing prediction accuracy and clinical applicability. The study addresses major challenges that hinder the adoption of advanced analytics in healthcare settings, including data quality limitations, insufficient real-time analytical capabilities, and low integration into routine clinical workflows. A comprehensive stakeholder analysis is conducted to align system functionality with the expectations of patients, clinicians, data scientists, IT professionals, and healthcare administrators, with particular emphasis on transparency, interpretability, and ease of use. The system architecture incorporates key functional components such as real-time prediction, automated alerts, and explainable visualization dashboards, alongside non-functional requirements focusing on interoperability, data privacy, and security.
Methodologically, the research adopts a hybrid development strategy integrating CRISP-DM, Software Development Life Cycle (SDLC), and agile methodologies to ensure the creation of scalable, reliable, and clinically relevant solutions. The findings demonstrate that the proposed system has significant potential to improve patient care, optimize healthcare resource utilization, and support timely, evidence-based clinical decision-making.