A new model of estimating fetal macrosomia based on neural network
Fetal macrosomia not only produces a great risk in delivery both to the mother and the fetus, but also has a bad influence to the future of the child. Prediction of fetal macrosomia has an important clinical meaning. In this paper, a new model of estimating fetal macrosomia is proposed. The aim of the model is to predict the fetal macrosomia, not the fetal weight. An artificial neural network is established to estimate the fetal macrosomia, the original data are trained and tested with the Bayesian Regularization method. The model gets an accuracy of 75％ with estimating fetal macrosomia.
macrosomia artificial neural network Bayesian Regularization
Xu Zhipeng Shen Aifang
School of Physics Science and Information Engineering,Liaocheng University Liaocheng, China Department of Gynaecology and Obstetrics Liaocheng Brain Hospital Liaocheng, China
国际会议
电子商务、工程及科学领域的分布计算和应用国际会议(DCABES 2010)
香港
英文
689-692
2010-08-10（万方平台首次上网日期，不代表论文的发表时间）