Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v8y2023i12p174-d1284184.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • María Claudia Bonfante & Juan Contreras Montes & Mariana Pino & Ronald Ruiz & Gabriel González, 2023. "Machine Learning Applications to Identify Young Offenders Using Data from Cognitive Function Tests," Data, MDPI, vol. 8(12), pages 1-15, November.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:12:p:174-:d:1284184
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/8/12/174/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/8/12/174/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Derek B. Rodgers & Deborah K. Reed & David E. Houchins & Ariel M. Aloe, 2020. "The writing abilities of juvenile justice youths: A confirmatory factor analysis," The Journal of Educational Research, Taylor & Francis Journals, vol. 113(6), pages 438-451, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:8:y:2023:i:12:p:174-:d:1284184. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.