Bonfante, M.C.; Montes, J.C.; Pino, M.; Ruiz, R.; González, G. Machine Learning Applications to Identify Young Offenders Using Data from Cognitive Function Tests. Data2023, 8, 174.
Bonfante, M.C.; Montes, J.C.; Pino, M.; Ruiz, R.; González, G. Machine Learning Applications to Identify Young Offenders Using Data from Cognitive Function Tests. Data 2023, 8, 174.
Bonfante, M.C.; Montes, J.C.; Pino, M.; Ruiz, R.; González, G. Machine Learning Applications to Identify Young Offenders Using Data from Cognitive Function Tests. Data2023, 8, 174.
Bonfante, M.C.; Montes, J.C.; Pino, M.; Ruiz, R.; González, G. Machine Learning Applications to Identify Young Offenders Using Data from Cognitive Function Tests. Data 2023, 8, 174.
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 non-delinquent 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 that aim to predict whether the data corresponds to that of a young offender or a non-offending youth. Prior to this, statistical tests were conducted on the data to identify characteristics that 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. 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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
