Machine Learning Applications to Identify Young Offenders Using Data from Cognitive Function Tests

María Claudia Bonfante, Juan Contreras Montes (), Mariana Pino, Ronald Ruiz and Gabriel González
Additional contact information
María Claudia Bonfante: Faculty of Engineering, Institución Universitaria de Barranquilla, Barranquilla 080002, Colombia
Juan Contreras Montes: Faculty of Engineering, Institución Universitaria de Barranquilla, Barranquilla 080002, Colombia
Mariana Pino: Faculty of Psychology, Universidad Autónoma del Caribe, Barranquilla 080020, Colombia
Ronald Ruiz: Faculty of Psychology, Universidad Autónoma del Caribe, Barranquilla 080020, Colombia
Gabriel González: Fundación Hogares Claret, Barranquilla 080002, Colombia

Data, 2023, vol. 8, issue 12, 1-15

Abstract: Machine learning techniques can be used to identify whether deficits in cognitive functions contribute to antisocial and aggressive behavior. This paper initially presents the results of tests conducted on delinquent and nondelinquent youths to assess their cognitive functions. The dataset extracted from these assessments, consisting of 37 predictor variables and one target, was used to train three algorithms which aim to predict whether the data correspond to those of a young offender or a nonoffending youth. Prior to this, statistical tests were conducted on the data to identify characteristics which exhibited significant differences in order to select the most relevant features and optimize the prediction results. Additionally, other feature selection methods, such as Boruta, RFE, and filter, were applied, and their effects on the accuracy of each of the three machine learning models used (SVM, RF, and KNN) were compared. In total, 80% of the data were utilized for training, while the remaining 20% were used for validation. The best result was achieved by the K-NN model, trained with 19 features selected by the Boruta method, followed by the SVM model, trained with 24 features selected by the filter method.

Keywords: cognitive functions; machine learning; feature selection; violence risk assessment (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/8/12/174/pdf (application/pdf)
https://www.mdpi.com/2306-5729/8/12/174/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:8:y:2023:i:12:p:174-:d:1284184

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().