• DocumentCode
    3135975
  • Title

    Risk Factors for Apgar Score using Artificial Neural Networks

  • Author

    Ibrahim, Doaa ; Frize, Monique ; Walker, Robin C.

  • Author_Institution
    Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont.
  • fYear
    2006
  • fDate
    Aug. 30 2006-Sept. 3 2006
  • Firstpage
    6109
  • Lastpage
    6112
  • Abstract
    Artificial Neural Networks (ANNs) have been used in identifying the risk factors for many medical outcomes. In this paper, the risk factors for low Apgar score are introduced. This is the first time, to our knowledge, that the ANNs are used for Apgar score prediction. The medical domain of interest used is the perinatal database provided by the Perinatal Partnership Program of Eastern and Southeastern Ontario (PPPESO). The ability of the feed forward back propagation ANNs to generate strong predictive model with the most influential variables is tested. Finally, minimal sets of variables (risk factors) that are important in predicting Apgar score outcome without degrading the ANN performance are identified
  • Keywords
    backpropagation; feedforward neural nets; health care; medical computing; medical information systems; obstetrics; prediction theory; risk analysis; Apgar score prediction; artificial neural networks; feed forward back propagation ANN; perinatal database; predictive model; risk factors; Artificial neural networks; Back; Cities and towns; Databases; Feeds; Partitioning algorithms; Pediatrics; Predictive models; Testing; USA Councils; Apgar score; Neural networks; perinatal outcomes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
  • Conference_Location
    New York, NY
  • ISSN
    1557-170X
  • Print_ISBN
    1-4244-0032-5
  • Electronic_ISBN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/IEMBS.2006.259591
  • Filename
    4463202