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法国样本中手臂骨的两性变异:比较多种统计模型,包括机器学习模型
Manon Blanc1,2, Siam Knecht3, Kathy Nguyen4
1Boulevard Tanguy Prigent, Centre Hospitalier Universitaire de Brest, Brest, 29200, France. manon1.blanc@chu-brest.fr.
International journal of legal medicine
|January 24, 2025
概括
使用手臂骨的法医性别估计非常准确,通过各种统计模型达到90%以上的准确性. 简化的模型和特定的肩膀骨测量提高了准确度至98-100%.
科学领域:
- 法医人类学 法医人类学
- 骨科 骨科是指骨科的学科.
- 生物识别信息 生物识别信息
背景情况:
- 在法医识别和骨分析中,性别估计至关重要.
- 传统上,骨盆的特征是使用的,但像骨这样的长骨也显示出显著的潜力.
- 骨遗骸的度量分析为确定性别提供了定量数据.
研究的目的:
- 为了比较七个统计模型的预测准确度,以基于部的性别估计.
- 用法语样本的度量分析来评估骨中的性二态.
- 为了确定最有效的统计技术和手臂骨测量来确定性别.
主要方法:
- 从法国样本中对98个部骨进行了26次测量,进行了度量分析.
- 七种统计模型的比较:线性差异分析 (LDA),规范差异分析 (RDA),惩罚后勤回归 (PLR),灵活差异分析 (FDA),支持矢量机 (SVM),人工神经网络 (ANN) 和随机森林 (RF).
- 使用交叉验证和模型简化来评估分类准确性.
主要成果:
- 所有模型在没有变量选择的情况下都实现了超过90%的分类准确性.
- 模型简化使用六个或更少的变量将准确性提高到98-100%.
- 处罚后勤回归 (PLR),随机森林 (RF) 和线性差异分析 (LDA) 显示出最高的准确性.
- 关键测量包括近端 (WTT,CSD),远端 (BEW,WT,MAW,THT) 和整个骨 (PLCT) 的尺寸.
结论:
- 软骨是法医背景下性别估计的一个可行的替代方案.
- 非经典的统计模型,特别是PLR,RF和LDA,为性别确定提供了强大的方法.
- 对骨测量的度量分析,特别是简化的模型,提供了高度准确的性别估计.
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