Authors - Vishnu Kumar, Natalia Miranda Abstract - Food insecurity remains a pressing public health and equity challenge in urban U.S. communities, with the Supplemental Nutrition Assistance Program (SNAP) serving as the primary federal mechanism for alleviating household food hardship. Despite its importance, SNAP participation varies substantially across neighborhoods, reflecting underlying socioeconomic disparities. This study leverages neighborhood-level data from Baltimore City to identify the key socioeconomic drivers of SNAP participation using explainable machine learning (ML) techniques. Three supervised ML models: Decision Tree, Random Forest, and XGBoost were developed and evaluated using standard regression metrics. The Random Forest model demonstrated the strongest predictive performance. Model interpretability was enhanced through Shapley Additive Explanations (SHAP), which quantified the contribution of each feature to predicted SNAP participation. Results indicate that lower income, shorter life expectancy, higher Temporary Assistance for Needy Families (TANF) participation, higher proportions of female-headed households, and lower educational attainment are associated with increased SNAP reliance. These findings highlight the complex interplay be-tween economic deprivation, social vulnerability, and neighborhood-level assistance utilization, offering actionable insights for policymakers and public health practitioners. By combining predictive accuracy with interpretability, explainable ML provides a robust framework for informing evidence-based interventions aimed at reducing food insecurity and promoting equity in urban communities.
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