Predicting Juvenile Delinquency and Criminal Behavior in Adulthood Using Machine Learning.

ABSTRACT: 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.

SUBMITTER: Schroeders U 

PROVIDER: S-EPMC12448129 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature