Leveraging Advanced Health Statistics for Predictive Modeling in Disease Prevention and Personalized Medicine

Abstract

his study explores the application of predictive modeling in disease prevention and personalized medicine by leveraging advanced health statistics. Despite the increasing availability of health data, existing models often fail to integrate patient-specific attributes effectively, creating a knowledge gap in personalized healthcare optimization. This research employs a statistical framework that enhances predictive capabilities by incorporating additional attributes from national health databases. Findings indicate that the proposed framework improves classification accuracy for high-risk subpopulations, enabling more efficient allocation of medical resources and targeted interventions. The results underscore the potential of advanced analytics in optimizing healthcare strategies, with implications for policy-making and resource distribution in public health.