Shikoku Medical College, Utazu, Kagawa 769-0205, Japan.
Department of Hygiene, Faculty of Medicine, Kagawa University, Miki, Kagawa 761-0793, Japan.
Medicina (Kaunas). 2019 Jun 9;55(6):259. doi: 10.3390/medicina55060259.
Many studies have reported on the causes of chronic lower back pain (CLBP). The aim of this study is to identify if the hierarchical neural network (HNN) is superior to a conventional statistical model for CLBP prediction. Linear models, which included multiple regression analysis, were executed for the analysis of the survey data because of the ease of interpretation. The problem with such linear models was that we could not fully consider the influence of interactions caused by a combination of nonlinear relationships and independent variables. The subjects in our study were 96 people (30 men aged 72.3 ± 5.6 years and 66 women aged 71.9 ± 5.4 years) who participated at a college health club from 20 July 2016 to 20 March 2017. The HNN and the logistic regression analysis (LR) were used for the prediction of CLBP and the accuracy of each analysis was compared and examined by using our previously reported data. The LR verified the fit using the Hosmer-Lemeshow test. The efficiencies of the two models were compared using receiver performance analysis (ROC), the root mean square error (RMSE), and the deviance (-2 log likelihood). The area under the ROC curve, the RMSE, and the -2 log likelihood for the LR were 0.7163, 0.2581, and 105.065, respectively. The area under the ROC curve, the RMSE, and the log likelihood for the HNN were 0.7650, 0.2483, and 102.787, respectively (the correct answer rates were HNN = 73.3% and LR = 70.8%). On the basis of the ROC curve, the RMSE, and the -2 log likelihood, the performance of the HNN for the prediction probability of CLBP is equal to or higher than the LR. In the future, the HNN may be useful as an index to judge the risk of CLBP for individual patients.
许多研究报告了慢性下腰痛（CLBP）的病因。本研究旨在确定分层神经网络（HNN）是否优于用于 CLBP 预测的传统统计模型。由于易于解释，线性模型（包括多元回归分析）用于分析调查数据。这种线性模型的问题在于，我们不能充分考虑由非线性关系和自变量组合引起的相互作用的影响。本研究的受试者为 96 人（30 名男性，年龄 72.3±5.6 岁，66 名女性，年龄 71.9±5.4 岁），他们于 2016 年 7 月 20 日至 2017 年 3 月 20 日参加了一个大学健康俱乐部。使用我们之前报告的数据，比较和检查了 HNN 和逻辑回归分析（LR）对 CLBP 的预测和每个分析的准确性。LR 通过 Hosmer-Lemeshow 检验验证拟合度。使用接收器性能分析（ROC）、均方根误差（RMSE）和偏差（-2 对数似然）比较两种模型的效率。LR 的 ROC 曲线下面积、RMSE 和-2 对数似然分别为 0.7163、0.2581 和 105.065。HNN 的 ROC 曲线下面积、RMSE 和对数似然分别为 0.7650、0.2483 和 102.787（正确答案率为 HNN=73.3%和 LR=70.8%）。基于 ROC 曲线、RMSE 和-2 对数似然，HNN 对 CLBP 预测概率的性能等于或高于 LR。在未来，HNN 可能作为判断个体患者 CLBP 风险的指标有用。
