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
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