Predicting juvenile delinquency and criminal behavior in adulthood using machine learning

By violating social norms, deviant behavior is an important issue that affects society as a whole and has serious consequences for its individuals. Different scientific disciplines have proposed theories of deviant behavior that often fall short of predicting actual behavior. In this registered report, we used data from the longitudinal National Study of Adolescent to Adult Health (Add Health) to examine the predictability of juvenile delinquency (Wave I) and adult criminal behavior (Wave V), distinguishing between drug, property, and violent offenses. Comparing the predictive accuracy of traditional regression models with different machine learning algorithms (elastic net regression and gradient boosting machines), we found the elastic net regressions with item-level data performed best. The prediction of juvenile delinquency was relatively accurate for drug offenses (R² = .57), violent offenses (R² = .44), and property offenses (R² = .39), while the performance declined significantly for adult delinquency, with R² values ranging from .16 to .13. Key predictors of juvenile delinquency versus adult criminal behavior were clearly different from each other. Early risk factors for adult criminal behavior included prior juvenile delinquency, particularly drug-related offenses, sex, and school-related issues such as suspension or expulsion. We discuss the findings in the context of relevant theories on the causes and development of criminal behavior and explore potential approaches for prevention and early intervention, particularly within the framework of the “Central Eight.”