• DocumentCode
    2048335
  • Title

    A New Model of Estimating Fetal Macrosomia Based on Neural Network

  • Author

    Zhipeng, Xu ; Aifang, Shen

  • Author_Institution
    Sch. of Phys. Sci. & Inf. Eng., Liaocheng Univ., Liaocheng, China
  • fYear
    2010
  • fDate
    10-12 Aug. 2010
  • Firstpage
    689
  • Lastpage
    692
  • Abstract
    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.
  • Keywords
    medical computing; neural nets; Bayesian regularization method; artificial neural network; fetal macrosomia; fetus; mother; neural network; Artificial neural networks; Equations; Fetus; Mathematical model; Neurons; Pediatrics; Training; Bayesian Regularization; artificial neural network; fetal macrosomia;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Distributed Computing and Applications to Business Engineering and Science (DCABES), 2010 Ninth International Symposium on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-7539-1
  • Type

    conf

  • DOI
    10.1109/DCABES.2010.142
  • Filename
    5570824
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2048335