| 徐新,谭红专,周书进,何玥,沈琳,柳祎,胡丽,王小娟,李逊.BP人工神经网络在早产预测模型中的应用[J].Chinese journal of Epidemiology,2014,35(9):1028-1031 |
| BP人工神经网络在早产预测模型中的应用 |
| Study on the application of Back-Propagation Artificial Neural Network used the model in predicting preterm birth |
| Received:March 24, 2014 |
| DOI：10.3760/cma.j.issn.0254-6450.2014.09.013 |
| KeyWord: 早产 神经网络 预测 |
| English Key Word: Premature birth Neural network Prediction |
| FundProject:国家自然科学基金（30872167） |
| Author Name | Affiliation | E-mail | | Xu Xin | School of Public Health, Central South University, Changsha 410008, China | | | Tan Hongzhuan | School of Public Health, Central South University, Changsha 410008, China | tanhz99@qq.com | | Zhou Shujin | Liuyang Hospital for Maternal and Child Health Care | | | He Yue | School of Public Health, Central South University, Changsha 410008, China | | | Shen Lin | School of Public Health, Central South University, Changsha 410008, China | | | Liu Yi | School of Public Health, Central South University, Changsha 410008, China | | | Hu Li | School of Public Health, Central South University, Changsha 410008, China | | | Wang Xiaojuan | School of Public Health, Central South University, Changsha 410008, China | | | Li Xun | School of Public Health, Central South University, Changsha 410008, China | |
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| Abstract: |
| 目的 基于BP人工神经网络的基本原理和方法，构建早产预测模型。方法 采用前瞻性队列研究方法，通过整群抽样，2010－2012年在湖南省浏阳市随机抽取怀孕妇女作为研究对象。 按2：1：1将调查样本随机分为训练样本、检验样本和测试样本，分别用于建立预测模型、选择最优神经网络和模型预测评价。采用BP人工神经网络和logistic回归分析建立模型，用ROC方法评价 所建立的早产预测模型的优劣。结果 整理6 270例分娩孕妇的数据，结果显示，早产265例，早产发生率为4.22%。将子宫异常及畸形、产次、妊娠胎数、妊娠期高血压、前置胎盘、胎膜早破和定期 产检7个多因素分析有统计学意义的变量选入预测模型。BP人工神经网络模型预测早产的灵敏度、特异度和一致率分别为67.65%、84.87%和84.12%，ROC曲线下面积为0.795；logistic回归模型预测早 产的灵敏度、特异度和一致率分别为64.71%、85.60%和84.69%，ROC曲线下面积为0.783。结论 新建立的BP人工神经网络模型实用可靠，其对早产的预测效能略优于logistic回归模型。 |
| English Abstract: |
| Objective To establish a practical and effective model in predicting the premature birth,using the Back-Propagation Artificial Neural Network (BPANN). Methods This was a prospective cohort study. Data was gathered from pregnant women selected by cluster sampling method from 2010 to 2012 in Liuyang city,Hunan province and was randomly divided into training sample (to establish the prediction models),validation sample (to select the optimal network) and testing sample (to evaluate the prediction models) by ratio of 2:1:1. BPANN and logistic regression analysis were used to establish models while ROC was applied to evaluate the ‘prediction models’. Results Among the 6 270 pregnant women,265 premature births were seen,with the premature incidence as 4.22%. The 7 variables which entered into the forecasting model would include abnormal uterine or uterine deformity,parity,number of pregnancies,gestational hypertension,placenta previa,premature rupture of membrane and regular prenatal examination. Sensitivity,specificity,agreement rate and area under the ROC curve of BPANN were 67.65%,84.87%,84.12% and 0.795,respectively. However,the sensitivity, specificity,agreement rate and area under the ROC curve of logistic regression were 64.71%,85.60%,84.69% and 0.783,respectively. Conclusion The newly established BPANN model was practical and reliable,which proved that this model was slightly better than the logistic regression in the prediction of premature birth. |
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