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Understanding sexual homicide in Korea using machine learning algorithms.

Hyeokjun Kwon1, Sanggyung Lee2, Hana Georgoulis3

  • 1Department of Psychology, Yeungnam University, Gyeongsan-si, Republic of Korea.

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This study used machine learning to identify key factors differentiating sexual homicide from other homicides in Korea. Relationship, planning, and crime scene details like nighttime occurrence are crucial indicators.

Keywords:
crime scene variablesdistinctionmachine learningnon‐sexual homicidesexual homicide

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Area of Science:

  • Criminology
  • Forensic Science
  • Machine Learning in Criminal Justice

Background:

  • Sexual homicide remains a complex area of study with limited research in Korea.
  • Differentiating sexual homicide from nonsexual homicide is crucial for effective criminal profiling and investigation.

Purpose of the Study:

  • To identify characteristics that distinguish sexual homicide from nonsexual homicide in the Korean context.
  • To explore the efficacy of machine learning algorithms in classifying sexual homicide cases.
  • To uncover patterns associated with sexual homicide in Korea.

Main Methods:

  • Analysis of 542 homicide cases using eight machine learning algorithms.
  • Evaluation of classification performance and variable importance for each algorithm.
  • Application of techniques such as Naive Bayes, K-Nearest Neighbors, and Random Forest (RF).

Main Results:

  • Naive Bayes, K-Nearest Neighbors, and RF algorithms showed strong classification accuracy.
  • Key differentiating variables included victim-offender relationships, marital status, premeditation, use of personal weapons, and overkill.
  • Crime scene factors like nighttime occurrence and body disposal were also significant predictors.

Conclusions:

  • Machine learning offers a powerful tool for understanding and classifying sexual homicide.
  • Identifying specific variables enhances the scientific basis for sexual homicide research and investigation in Korea.
  • This study provides a foundation for improving crime investigation efficacy through a data-driven approach to sexual homicide.