Human Variation and Identification Research Unit, School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, 7 York Road, Parktown, Johannesburg, 2193, South Africa.
Basic Medical Sciences, School of Biomedical Sciences, Faculty of Health Sciences, University of the Free State, South Africa.
Anthropol Anz. 2024 Mar 21;81(2):233-239. doi: 10.1127/anthranz/2023/1737.
Diagnosis of specific infectious diseases in the skeleton is often difficult and relies on expert opinion. Statistics is not often used as a tool to assist in such diagnoses, and therefore this study aimed at employing data mining and machine learning in the form of decision tree analysis to aid in recognizing tuberculosis (TB) in skeletal remains and find patterns of skeletal involvement. The sample included 387 modern South African individuals (n = 207 individuals known to have died of TB and n = 180 as a control group) which were scored for the presence or absence of 21 skeletal lesions documented to be associated with TB. A pruned decision tree classification analysis was done to detect significant patterns and associations between variables which produced a model with a moderate classification rate based on four of the variables. As expected, vertebral changes were selected first, followed by rib, acetabular and lastly cranial changes. As a proof of concept, it was shown that machine learning was able to identify patterns of changes in TB skeletons versus a control group. However, further investigation into the use of machine learning in assessing skeletal changes associated with specific diseases is needed.
在骨骼中诊断特定的传染病通常很困难，需要依靠专家意见。统计数据通常不作为辅助此类诊断的工具，因此本研究旨在采用数据挖掘和决策树分析形式的机器学习，以帮助识别骨骼中的结核病（TB）并发现骨骼受累的模式。该样本包括 387 名现代南非个体（n=207 名已知死于结核病的个体和 n=180 名对照组个体），这些个体的 21 种骨骼病变存在或不存在被评分，这些病变被记录为与结核病有关。进行了修剪决策树分类分析，以检测变量之间的显著模式和关联，从而产生了一个基于四个变量的具有中等分类率的模型。正如预期的那样，首先选择了脊柱变化，其次是肋骨、髋臼，最后是颅骨变化。作为概念验证，表明机器学习能够识别结核病骨骼与对照组之间的变化模式。然而，需要进一步研究机器学习在评估与特定疾病相关的骨骼变化方面的应用。
