AI-driven risk stratification for targeted public health interventions

The increasing complexity of public health challenges, particularly during infectious disease outbreaks, necessitates innovative approaches to identify and protect vulnerable populations. This study explores the use of supervised machine learning algorithms to stratify populations based on risk factors and predict severe outcomes during outbreaks. By leveraging demographic, clinical, and socioeconomic data, the proposed AI-driven models aim to enable healthcare systems to prioritize vulnerable groups, allocate resources effectively, and implement preventive measures. The results demonstrate the potential of AI in reducing mortality, improving health equity, and enhancing the overall resilience of public health systems. This research contributes to the growing body of knowledge on data-driven decision-making in public health.