Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemoonan-ro, Gonro-gu, Seoul, 03181, Republic of Korea.
Arch Osteoporos. 2020 Oct 23;15(1):169. doi: 10.1007/s11657-020-00802-8.
Many predictive tools have been reported for assessing osteoporosis risk. The development and validation of osteoporosis risk prediction models were supported by machine learning.
Osteoporosis is a silent disease until it results in fragility fractures. However, early diagnosis of osteoporosis provides an opportunity to detect and prevent fractures. We aimed to develop machine learning approaches to achieve high predictive ability for osteoporosis risk that could help primary care providers identify which women are at increased risk of osteoporosis and should therefore undergo further testing with bone densitometry.
We included all postmenopausal Korean women from the Korea National Health and Nutrition Examination Surveys (KNHANES V-1, V-2) conducted in 2010 and 2011. Machine learning models using methods such as the k-nearest neighbors (KNN), decision tree (DT), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), artificial neural networks (ANN), and logistic regression (LR) were developed to predict osteoporosis risk. We analyzed the effect of applying the machine learning algorithms to the raw data and featuring the selected data only where the statistically significant variables were included as model inputs. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were used to evaluate performance among the seven models.
A total of 1792 patients were included in this study, of which 613 had osteoporosis. The raw data consisted of 19 variables and achieved performances (in terms of AUROCs) of 0.712, 0.684, 0.727, 0.652, 0.724, 0.741, and 0.726 for KNN, DT, RF, GBM, SVM, ANN, and LR with fivefold cross-validation, respectively. The feature selected data consisted of nine variables and achieved performances (in terms of AUROCs) of 0.713, 0.685, 0.734, 0.728, 0.728, 0.743, and 0.727 for KNN, DT, RF, GBM, SVM, ANN, and LR with fivefold cross-validation, respectively.
In this study, we developed and compared seven machine learning models to accurately predict osteoporosis risk. The ANN model performed best when compared to the other models, having the highest AUROC value. Applying the ANN model in the clinical environment could help primary care providers stratify osteoporosis patients and improve the prevention, detection, and early treatment of osteoporosis.
已有多种预测工具被报道用于评估骨质疏松症风险。机器学习支持骨质疏松症风险预测模型的开发和验证。
我们旨在开发机器学习方法以实现对骨质疏松症风险的高预测能力，帮助初级保健提供者识别哪些女性骨质疏松症风险增加，因此应进一步进行骨密度检查。
我们纳入了 2010 年和 2011 年进行的韩国国家健康和营养检查调查（KNHANES V-1、V-2）中的所有绝经后韩国女性。使用 K 近邻（KNN）、决策树（DT）、随机森林（RF）、梯度提升机（GBM）、支持向量机（SVM）、人工神经网络（ANN）和逻辑回归（LR）等方法开发了机器学习模型，以预测骨质疏松症风险。我们分析了将机器学习算法应用于原始数据和仅选择包含作为模型输入的统计学显著变量的数据的效果。准确性、敏感性、特异性和接收器工作特征曲线下的面积（AUROC）用于评估七种模型之间的性能。
共有 1792 名患者纳入本研究，其中 613 名患有骨质疏松症。原始数据包含 19 个变量，五重交叉验证的 AUROC 分别为 0.712、0.684、0.727、0.652、0.724、0.741 和 0.726，用于 KNN、DT、RF、GBM、SVM、ANN 和 LR。特征选择数据包含 9 个变量，五重交叉验证的 AUROC 分别为 0.713、0.685、0.734、0.728、0.728、0.743 和 0.727，用于 KNN、DT、RF、GBM、SVM、ANN 和 LR。
在本研究中，我们开发并比较了七种机器学习模型以准确预测骨质疏松症风险。与其他模型相比，ANN 模型表现最佳，具有最高的 AUROC 值。在临床环境中应用 ANN 模型可以帮助初级保健提供者对骨质疏松症患者进行分层，并改善骨质疏松症的预防、检测和早期治疗。
