中国全科医学2017，Vol.20Issue(27)：3410-3415,6.DOI:10.3969/j.issn.1007-9572.2017.06.y01
基于人工神经网络的卵巢早衰预测模型研究
Prediction Model of Premature Ovarian Failure Based on Artificial Neural Network
摘要
Abstract
Objective To establish a multilayer forward neural network model based on artificial neural network(ANN) for predicting premature ovarian failure(POF),in order to increase the total coincidental rate of clinical diagnosis.Methods Three hundred and forty-one women met the inclusion criteria were selected as the study subjects,who lived in six communities under the jurisdiction of Baiyushan Street in Wuhan City during January to March 2011.From May 2011 to June 2016,each case was conducted to hospital follow-up once every four months until the age of 40.During the follow-up,2 cases underwent hysterectomy,2 cases received sex hormone treatment,21 cases were lost,all these cases were excluded,and finally 316 cases were included in the study.Using unbiased randomized allocation method,316 subjects were divided into training sample(177 cases),test sample(44 cases) and persistent sample(95 cases).The input parameters were set as the type A behavior,mumps history,history of gynecological surgery,history of ovulation induction drugs use,marriage and birth history,follicle stimulating hormone(FSH),FSH/luteinizing hormone(LH) and anti mullerian hormone(AMH),inhibin B(INHB),the number of antral follicles(AFC),systolic peak velocity(PSV) and resistance index(RI);the output parameter was "whether POF occured".The model was constructed by training sample,and corrected by the test sample.The stability of the model was tested by persistent sample.Results After eliminating the "redundancy",ANN automatically constructed model of input unit(12),single hidden layer(six nodes) and activation function(hyperbolic tangent),output unit(2) and activation function(softmax).The cross entropy error value of the training sample was 53.236.Abort testing when the prediction error did not decrease,and the test time was 0.42 s.The input parameters affecting the weights in the top 5 were AMH(26.3%),INHB(24.1%),AFC(21.7%),type A behavior(7.2%),and history of gynecological surgery(6.5%).The sensitivity of multilayer forward neural network model predicting POF in the training sample,test sample and persistent sample was 97.8%,91.7% and 92.0%,respectively;the specificity was 92.4%,84.4% and 80.0%,respectively,and the total coincidental rate was 93.8%,86.4% and 83.2%,respectively.On the basis of training sample and test sample,the AUC of multilayer forward neural network model predicting POF was 0.972.Conclusion The multilayer forward neural network model based on ANN for predicting POF has a high total coincidental rate of clinical diagnosis.It not only provides a theoretical basis and method support for the clinical efficient diagnosis and optimization examination,but also provides an opportunity to realize early prevention and early treatment,and is worthy of clinical promotion.关键词
原发性卵巢功能不全/卵巢功能早衰/神经网络(计算机)/预测Key words
Primary ovarian insufficiency/Ovarian failure/premature/Neural networks(computer)/Forecasting分类
医药卫生引用本文复制引用
吴妍,姚蕾,盛文丽,刘明娟..基于人工神经网络的卵巢早衰预测模型研究[J].中国全科医学,2017,20(27):3410-3415,6.基金项目
武汉市临床医学科研项目(WX15D15) （WX15D15）
第四批武汉中青年医学骨干人才资助项目 （）
