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Abstract
Predictive analytics in healthcare has emerged as a transformative approach to improving clinical decision-making, operational efficiency, and patient outcomes. By leveraging historical and real-time data, predictive models can forecast critical events such as disease onset, hospital readmissions, adverse drug reactions, and demand surges for healthcare resources. The proposed research develops an AI-powered predictive analytics system designed for proactive and efficient healthcare management and decision support. Unlike traditional statistical methods, advanced machine learning and deep learning models enable the detection of complex, non-linear interactions, offering greater accuracy and clinical relevance. The study systematically addresses key challenges, including poor data quality, limited adoption of advanced analytics in clinical practice, and lack of real-time predictive capabilities. A comprehensive stakeholder analysis ensures alignment with the needs of patients, clinicians, data scientists, IT teams, and administrators, emphasizing trust, interpretability, and usability. The system design integrates functional requirements such as real-time predictions, alerting mechanisms, explainable dashboards with non-functional requirements focused on interoperability, privacy, and security. Methodologically, the work employs a hybrid approach combining CRISP-DM, SDLC, and agile practices to ensure robust, scalable, and clinically viable solutions. Results demonstrate the system’s potential to enhance patient care, optimize hospital resources, and support informed, timely healthcare decisions.