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Ancestry assessment using random forest modeling.

Joseph T Hefner1, M Kate Spradley, Bruce Anderson

  • 1JPAC CIL, 310 Worchester Blvd., BLDG 45, Joint Base Pearl Harbor-Hickam, HI, 96853-5530.

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|February 8, 2014
PubMed
Summary
This summary is machine-generated.

Forensic anthropology can determine ancestry using skeletal traits and measurements. Combining both methods with a random forest model (RFM) improves accuracy in classifying unknown crania.

Keywords:
ancestrycraniometricsforensic anthropologyforensic sciencemorphoscopic traitsquantitative methodsrandom forest model

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Area of Science:

  • Forensic anthropology
  • Biological anthropology
  • Population genetics

Background:

  • Skeletal assessment of ancestry traditionally relies on morphoscopic traits and measurements.
  • Previous studies have explored these data types independently for population comparisons.

Purpose of the Study:

  • To investigate if morphoscopic traits and skeletal measurements provide similar biological information for ancestry assessment.
  • To combine both data types into a single classification model for improved accuracy.
  • To compare the efficacy of a random forest model (RFM) against traditional methods.

Main Methods:

  • Utilized morphoscopic traits and skeletal measurements from American Black, American White, and Southwest Hispanic individuals.
  • Employed discriminant analyses and a random forest model (RFM) for classification.
  • Performed cross-validation to assess classification accuracy.

Main Results:

  • Both morphoscopic traits and skeletal measurements yield similar information regarding population group relationships.
  • Combining both data types in an RFM significantly increases the correct allocation of ancestry for unknown crania.
  • The RFM achieved a 89.6% correct classification rate, outperforming discriminant function analysis (75.4%) using only morphoscopic data.

Conclusions:

  • The integration of morphoscopic traits and skeletal measurements offers a quantifiable approach to ancestry assessment.
  • This combined method accounts for greater variation within and between groups, reducing misclassification rates.
  • The RFM approach captures cranial shape, size, and morphology more comprehensively than traditional methods.