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An automated system for the assessment and grading of adolescent delinquency using a machine learning-based soft voting framework

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  • Abhinash Jenasamanta

    (Birla Institute of Technology)

  • Subrajeet Mohapatra

    (Birla Institute of Technology)

Abstract

Adolescent (or juvenile) delinquency is defined as the habitual engagement in unlawful behavior of a minor under the age of majority. According to studies, the likelihood of acquiring a deviant personality increases significantly during adolescence. As a result, identifying deviant youth early and providing proper medical counseling makes perfect sense. Due to the scarcity of qualified clinicians, human appraisal of individual adolescent behavior is subjective and time-consuming. As a result, a machine learning-based intelligent automated system for assessing and grading delinquency levels in teenagers at an early stage must be devised. To solve this problem, a soft voting-based ensemble classification model has been developed that includes a Decision Tree, Multi-layer Perceptron, and Support Vector Machine as base classifiers to accurately classify teenagers into three groups based on severity levels, viz., low, medium, and high. Over the normalized structured behavioral data, the proposed soft voting-based model outperforms all other individual classifiers with 87.50% accuracy, an AUC of 0.94, 0.81 Kappa value, and an F-score of 0.88.

Suggested Citation

  • Abhinash Jenasamanta & Subrajeet Mohapatra, 2022. "An automated system for the assessment and grading of adolescent delinquency using a machine learning-based soft voting framework," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    • Handle: RePEc:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01407-x
    DOI: 10.1057/s41599-022-01407-x
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    References listed on IDEAS

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    1. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    2. Bruce G. Marcot & Anca M. Hanea, 2021. "What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?," Computational Statistics, Springer, vol. 36(3), pages 2009-2031, September.
    3. Sciandra, Matthew & Sanbonmatsu, Lisa & Duncan, Greg J. & Gennetian, Lisa A. & Katz, Lawrence F. & Kessler, Ronald & Kling, Jeffrey R. & Ludwig, Jens, 2013. "Long-Term Effects of the Moving to Opportunity Residential Mobility Experiment on Crime and Delinquency," Scholarly Articles 34222823, Harvard University Department of Economics.
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    Cited by:

    1. Juan David Gelvez, 2025. "Predicting police and military violence: evidence from Colombia and Mexico using machine learning models," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-13, December.

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