This study describes the development and validation of a machine learning model (random forest) to predict the risk of firearm-related injuries across Chicago neighborhoods. By combining multi-source data, including local crime reports, socioeconomic vulnerabilities, and environmental exposures, the model maps out areas with the highest predicted risk.
The model’s predictions were validated against actual administrative records to verify its spatial accuracy. This predictive modeling framework shows how machine learning can help public health officials and community organizations anticipate high-risk spatial hot spots and deploy community-based interventions more effectively.