{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:32:55Z","timestamp":1768415575859,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Instituci\u00f3n Universitaria de Barranquilla and Ministerio de Ciencia Tecnolog\u00eda e Innovaci\u00f3n in Colombia","award":["68229 BPIN 20200000100006"],"award-info":[{"award-number":["68229 BPIN 20200000100006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/data8120174","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T01:48:45Z","timestamp":1700531325000},"page":"174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning Applications to Identify Young Offenders Using Data from Cognitive Function Tests"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7884-6482","authenticated-orcid":false,"given":"Mar\u00eda Claudia","family":"Bonfante","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Instituci\u00f3n Universitaria de Barranquilla, Barranquilla 080002, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9730-9263","authenticated-orcid":false,"given":"Juan Contreras","family":"Montes","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Instituci\u00f3n Universitaria de Barranquilla, Barranquilla 080002, Colombia"}]},{"given":"Mariana","family":"Pino","sequence":"additional","affiliation":[{"name":"Faculty of Psychology, Universidad Aut\u00f3noma del Caribe, Barranquilla 080020, Colombia"}]},{"given":"Ronald","family":"Ruiz","sequence":"additional","affiliation":[{"name":"Faculty of Psychology, Universidad Aut\u00f3noma del Caribe, Barranquilla 080020, Colombia"}]},{"given":"Gabriel","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Fundaci\u00f3n Hogares Claret, Barranquilla 080002, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"ref_1","first-page":"70","article-title":"Antisocial Behavior and Executive Functions in Young Offenders","volume":"23","year":"2018","journal-title":"Rev. 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