ArticleInternational journal of behavioral development2025
Predicting Juvenile Delinquency and Criminal Behavior in Adulthood Using Machine Learning.
Article in International journal of behavioral development, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Predicting wellbeing in empty-nesters: an ensemble machine learning approach with data-driven feature selection.Frontiers in psychology · 2026Article
- Multilevel Correlates of Youth Delinquent Behaviors: An Exploratory Analysis of Socioeconomic Context, Psychological Traits, and Task-Based Neural Activation in the ABCD Study.Social science insights and applications · 2025Article
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Authors and funding
4 authors.
Funding
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. The prediction of juvenile delinquency was relatively accurate for drug offenses (
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