THE ROLE OF MACHINE LEARNING IN PREDICTIVE HEALTHCARE INFORMATION SYSTEMS

Abstract

Machine learning (ML) has emerged as a transformative technology in predictive healthcare information systems, enabling more accurate diagnostics, personalized treatments, and enhanced patient care. As healthcare systems become more complex, the integration of ML algorithms into predictive analytics frameworks has proven instrumental in optimizing decision-making processes. This article explores the application of machine learning models in healthcare, focusing on predictive healthcare information systems. The key areas covered include disease prediction, risk stratification, personalized medicine, and the optimization of healthcare resources. Through various ML techniques, including supervised and unsupervised learning, healthcare providers can anticipate health outcomes, leading to more efficient interventions and improved patient prognosis. This study also highlights challenges such as data privacy concerns, model interpretability, and integration complexities within existing healthcare infrastructure. Furthermore, we provide a detailed analysis of the impact of ML on healthcare operations in Pakistan, exploring local innovations and the potential for future advancements.