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.
fDate :
Aug. 30 2006-Sept. 3 2006
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;
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2006.259591