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Predicting and explaining recurrent child abuse using interpretable machine learning: Evidence from national-level child abuse data in South Korea (2017–2020)

Author

Listed:
  • Kim, Donghun
  • Jiang, Ting
  • Kim, Kihyun
  • Kim, Jae-Won
  • Zhu, Yongjun

Abstract

This study aimed to develop machine learning models to predict the risk of each type of recurrent child abuse using national-level child abuse data. Additionally, it sought to identify key factors based on abuse type and explain recurrence risk in individual cases.

Suggested Citation

  • Kim, Donghun & Jiang, Ting & Kim, Kihyun & Kim, Jae-Won & Zhu, Yongjun, 2026. "Predicting and explaining recurrent child abuse using interpretable machine learning: Evidence from national-level child abuse data in South Korea (2017–2020)," Social Science & Medicine, Elsevier, vol. 389(C).
    • Handle: RePEc:eee:socmed:v:389:y:2026:i:c:s0277953625011591
    DOI: 10.1016/j.socscimed.2025.118828
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    References listed on IDEAS

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    1. Rittner, Barbara, 2002. "The use of risk assessment instruments in child protective services case planning and closures," Children and Youth Services Review, Elsevier, vol. 24(3), pages 189-207, March.
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