PDF(Pubmed)
Abstract：
Machine learning (ML) models have been increasingly employed to predict osteoporosis. However, the incorporation of hair minerals into ML models remains unexplored. This study aimed to develop ML models for predicting low bone mass (LBM) using health checkup data and hair mineral analysis. A total of 1206 postmenopausal women and 820 men aged 50 years or older at a health promotion center were included in this study. LBM was defined as a T-score below - 1 at the lumbar, femur neck, or total hip area. The proportion of individuals with LBM was 59.4% (n = 1205). The features used in the models comprised 50 health checkup items and 22 hair minerals. The ML algorithms employed were Extreme Gradient Boosting (XGB), Random Forest (RF), Gradient Boosting (GB), and Adaptive Boosting (AdaBoost). The subjects were divided into training and test datasets with an 80:20 ratio. The area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and an F1 score were evaluated to measure the performances of the models. Through 50 repetitions, the mean (standard deviation) AUROC for LBM was 0.744 (± 0.021) for XGB, the highest among the models, followed by 0.737 (± 0.023) for AdaBoost, and 0.733 (± 0.023) for GB, and 0.732 (± 0.021) for RF. The XGB model had an accuracy of 68.7%, sensitivity of 80.7%, specificity of 51.1%, PPV of 70.9%, NPV of 64.3%, and an F1 score of 0.754. However, these performance metrics did not demonstrate notable differences among the models. The XGB model identified sulfur, sodium, mercury, copper, magnesium, arsenic, and phosphate as crucial hair mineral features. The study findings emphasize the significance of employing ML algorithms for predicting LBM. Integrating health checkup data and hair mineral analysis into these models may provide valuable insights into identifying individuals at risk of LBM.
摘要：
机器学习（ML）模型已越来越多地用于预测骨质疏松症。然而,将头发矿物质掺入ML模型仍未探索。这项研究旨在开发ML模型，用于使用健康检查数据和头发矿物质分析来预测低骨量（LBM）。本研究纳入健康促进中心共有1206名绝经后女性和820名50岁以上男性。LBM被定义为腰椎的T评分低于-1，股骨颈,或总髋关节面积。患有LBM的个体比例为59.4%(n=1205)。模型中使用的功能包括50项健康检查项目和22种头发矿物质。采用的ML算法是极端梯度提升(XGB)，随机森林(RF),梯度提升(GB),和自适应提升（AdaBoost）。将受试者分为80:20比例的训练和测试数据集。接收器工作特征曲线下面积(AUROC)，准确度,灵敏度,特异性，阳性预测值(PPV)，评估阴性预测值(NPV)和F1评分以衡量模型的性能.通过50次重复，LBM的平均（标准差）AUROC为XGB的0.744（±0.021），模特中最高的，其次是0.737（±0.023）的AdaBoost，GB为0.733(±0.023)，射频为0.732(±0.021)。XGB模型的准确率为68.7%，灵敏度为80.7%，特异性为51.1%，PPV为70.9%，NPV为64.3%，F1得分为0.754。然而,这些性能指标在模型之间没有显着差异.XGB模型确定了硫，钠,水银,铜，镁，砷,和磷酸盐作为至关重要的头发矿物特征。研究结果强调了采用ML算法预测LBM的重要性。将健康检查数据和头发矿物质分析集成到这些模型中可以为识别处于LBM风险的个体提供有价值的见解。
