Can Machine Learning Improve Screening for Targeted Delinquency Prevention Programs?

ABSTRACT: The cost-effectiveness of targeted delinquency prevention programs for children depends on the accuracy of the screening process. Screening accuracy is often poor, resulting in wasted resources and missed opportunities to avert negative outcomes. This study examined whether screening approaches based on logistic regression or machine learning algorithms could improve accuracy relative to traditional sum-score approaches when identifying boys in the 5th grade (N = 1012) who would be repeatedly arrested for violent and serious crimes from ages 13 to 30. Screening algorithms were developed that incorporated facets of teacher-reported externalizing problems and other known risk factors (e.g., peer rejection). The predictive performance of these algorithms was evaluated and compared in holdout

SUBMITTER: Pelham WE 

PROVIDER: S-EPMC7041896 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature