Henriques Mélanie, Bonhomme Vincent, Cunha Eugénia, Adalian Pascal
Centre for Functional Ecology (CEF), Laboratory of Forensic Anthropology, Department of Life Sciences, University of Coimbra, 3000-456 Coimbra, Portugal.
Aix Marseille Univ, CNRS, EFS, ADES, 13007 Marseille, France.
Biology (Basel). 2023 Jan 29;12(2):206. doi: 10.3390/biology12020206.
In this study, we propose a classification method between falls and blows using random forests. In total, 400 anonymized patients presenting with fractures from falls or blows aged between 20 and 49 years old were used. There were 549 types of fractures for 57 bones and 12 anatomical regions observed. We first tested various models according to the sensibility of random forest parameters and their effects on model accuracies. The best model was based on the binary coding of 12 anatomical regions or 28 bones with or without baseline (age and sex). Our method achieved the highest accuracy rate of 83% in the distinction between falls and blows. Our findings pave the way for applications to help forensic experts and archaeologists.
在本研究中，我们提出了一种使用随机森林对跌倒和撞击进行分类的方法。总共使用了400名年龄在20至49岁之间因跌倒或撞击而骨折的匿名患者。观察到57块骨骼和12个解剖区域存在549种骨折类型。我们首先根据随机森林参数的敏感性及其对模型准确性的影响测试了各种模型。最佳模型基于12个解剖区域或28块骨骼的二进制编码，有或没有基线（年龄和性别）。我们的方法在区分跌倒和撞击方面达到了最高准确率83%。我们的研究结果为应用于帮助法医专家和考古学家铺平了道路。
