Development of integrated machine learning models for multi-disease prediction

The increasing burden of infectious diseases on global public health systems necessitates innovative approaches for early prediction and intervention. This study focuses on the development of ensemble machine learning models to predict multiple infectious diseases, leveraging diverse datasets to provide actionable insights for public health policymakers. By integrating data from demographic, environmental, and clinical sources, the proposed models aim to identify high-risk individuals and regions, enabling targeted vaccination campaigns and optimized resource allocation during outbreaks. The results demonstrate the potential of machine learning in enhancing public health resilience, reducing disease transmission, and improving healthcare outcomes. This research contributes to the growing body of knowledge on data-driven decision-making in public health.