Predictive Modeling of Racial Disparities in U.S. Violent Deaths

Music City Center 

Violent death rates in the United States exhibit pronounced racial disparities that challenge the healthcare, insurance, and public safety sectors. These disparities, shaped by demographics, mental health, substance abuse, and geography, complicate practical risk assessment and targeted interventions. Leveraging data from the National Violent Death Reporting System (NVDRS) for 2020–2021, this study examines racial differences in suicides, homicides, and other violent deaths. Logistic regression models assess the effects of race, age, sex, mental health, substance use, and state-level variability. The results are compared with several machine learning models to evaluate the trade-off between predictive performance and interpretability. Guided by the Social Determinants of Health and structured with the Design Science Framework, findings reveal that logistic regression delivers interpretable, actionable insights while achieving competitive accuracy and sensitivity. These insights enhance our understanding of violent death outcomes and support the development of refined risk profiles and targeted business solutions for high-risk groups.

MORTALITY

RACE

LOGISTIC REGRESSION

MACHINE LEARNING

VIOLENT DEATHS

UNITED STATES 

Business Analytics/Statistics Education Interest Group