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Data mining for sex estimation based on cranial measurements.

Diana H Toneva1, Silviya Y Nikolova1, Gennady P Agre2

  • 1Department of Anthropology and Anatomy, Institute of Experimental Morphology, Pathology and Anthropology with Museum, Bulgarian Academy of Sciences, 1113, Sofia, Bulgaria.

Forensic Science International
|August 12, 2020
PubMed
Summary

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This study developed accurate sex estimation models using cranial measurements and data mining. Rule-based models, particularly JRIP, achieved over 91% accuracy, outperforming logistic regression and decision trees for forensic anthropology applications.

Area of Science:

  • Forensic Anthropology
  • Medical Imaging
  • Data Mining

Background:

  • Accurate sex estimation from skeletal remains is crucial in forensic anthropology.
  • Traditional methods rely on subjective assessments or limited measurements.
  • Developing objective, data-driven approaches is essential for improving accuracy.

Purpose of the Study:

  • To develop effective and understandable classification models for sex estimation using adult cranial measurements.
  • To identify the most dimorphic linear cranial measurements for sex estimation.
  • To compare the performance of machine learning models against logistic regression.

Main Methods:

  • Utilized computed tomography (CT) scans from 393 adult individuals.
  • Applied a landmark-based approach to collect 47 landmarks and derive cranial measurements (37 standard, 1081 interlandmark distances).
Keywords:
Classification rulesCranial measurementsDecision treesForensic anthropology population dataMachine learningSex estimation

Related Experiment Videos

  • Employed data mining algorithms (JRIP, Ridor, J48) and attribute selection methods (Weka BestFirst, Weka GeneticSearch).

    • Main Results:

    • The JRIP algorithm, using standard cranial measurements selected by GeneticSearch, achieved the highest accuracy of 91.9%.
    • Rule-based models demonstrated superior performance compared to logistic regression and decision tree models.
    • For non-standard measurements, JRIP and Ridor achieved accuracies of 88.3% with specific preprocessing methods.

    Conclusions:

    • Rule-based data mining models offer a highly accurate and efficient method for sex estimation from cranial data.
    • These models provide a more objective and potentially more reliable alternative to traditional methods.
    • The study identified specific cranial measurements that are highly dimorphic and useful for sex estimation.