Background: Not only in India but also worldwide, criminal activity has dramatically increasing day by day among youth, and it must be addressed properly to maintain a healthy society. This review is focused on risk factors and quantitative approach to determine delinquent behaviors of juveniles.
Materials and methods: A total of 15 research articles were identified through Google search as per inclusion and exclusion criteria, which were based on machine learning (ML) and statistical models to assess the delinquent behavior and risk factors of juveniles.
Results: The result found ML is a new route for detecting delinquent behavioral patterns. However, statistical methods have used commonly as the quantitative approach for assessing delinquent behaviors and risk factors among juveniles.
Conclusions: In the current scenario, ML is a new approach of computer-assisted techniques have potentiality to predict values of behavioral, psychological/mental, and associated risk factors for early diagnosis in teenagers in short of times, to prevent unwanted, maladaptive behaviors, and to provide appropriate intervention and build a safe peaceful society.
Keywords: Delinquent behavior; juvenile-delinquency; machine learning; risk-factors.
Copyright: © 2022 Indian Journal of Community Medicine.