Intelligent Data Mining and Predictive Models for Healthcare Innovation

Abstract

Healthcare industry is in middle of a large transformation driven by digital data, machine learning models, and predictive analytics. Intelligent data mining techniques combined with advanced predictive models offer opportunity for creating new pathways in disease diagnosis, treatment optimization, and patient care delivery. This review paper critically examines the applications of intelligent data mining in healthcare with special attention to predictive models for early disease detection, hospital resource management, and personalized medicine. The paper highlights major contributions of machine learning algorithms, natural language processing, and big data integration in healthcare environments. In addition, it discusses the challenges such as algorithmic bias, data privacy, interoperability issues, and ethical concerns that make implementation difficult. Critical insights are provided on how intelligent data mining reshapes modern healthcare, along with future implications for clinical decision support systems and preventive medicine. The review concludes that predictive models, if properly integrated, will stand as foundation for more patient-centered, efficient, and sustainable healthcare systems.

KEYWORDS: Intelligent Data Mining; Predictive Models; Healthcare Innovation; Machine Learning; Patient-Centric Systems