可解释的机器学习用于从长骨中进行个性化的性别估计
Siam Knecht1, Paolo Morandini2, Lucie Biehler-Gomez2
1Aix Marseille Univ, CNRS, EFS, ADES, Marseille, France. siam.knecht@univ-amu.fr.
International journal of legal medicine
|October 24, 2025
概括
机器学习 (ML) 通过使用长骨测量,准确地从骨遗骸中估计性别. 像线性差异分析 (LDA) 这样的可解释模型提供了透明的,个性化的法医证据.
科学领域:
- 法医人类学 法医人类学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 性别估计对于从骨遗骸中识别个体至关重要.
- 准确的性别估计提高了其他生物特征分析的可靠性,例如年龄和身材.
- 当前的方法面临着解释性挑战,特别是人工智能 (AI) 模型.
研究的目的:
- 研究机器学习 (ML) 技术的应用,以从骨遗骸中估计性别.
- 通过专注于模型可解释性来解决人工智能的"黑子"问题.
- 在法医性别估计中评估不同ML算法的准确性和透明度.
主要方法:
- 利用了从2,969个人的长骨测量数据集.
- 评估了12种不同的机器学习算法.
- 采用代回归归算法来处理缺失的数据和夏普利添加式扩展 (SHAP) 来实现模型的解释性.
主要成果:
- 线性差异分析 (LDA) 获得了最高的准确率95.2%.
- SHAP值提供了对预测因素的个性化见解,提高了模型的透明度.
- 缺失的数据显著影响了预测的准确性,突出了对强大的数据处理的需求.
结论:
- 机器学习模型可以有效地平衡法医性别估计中的准确性和可解释性.
- 可解释的人工智能模型为法律和科学标准提供了个性化,可操作的见解.
- 这项研究在法医科学中推进了人工智能驱动的方法,以获得可辩护的,个性化的证据.
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