title: Risk factors for Apgar score using artificial neural networks.
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.
authors.affiliation: Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont., Canada. dibrahin@site.uottawa.ca
authors.forename: Doaa
authors.initials: D
authors.lastname: Ibrahim
authors.forename: Monique
authors.initials: M
authors.lastname: Frize
authors.forename: Robin C
authors.initials: RC
authors.lastname: Walker
mesh_terms: D000465:Algorithms; D001034:Apgar Score; D001185:Artificial Intelligence; D001331:Automation; D016208:Databases, Factual; D006801:Humans; D007231:Infant, Newborn; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009864:Ontario; D017063:Outcome Assessment, Health Care; D010363:Pattern Recognition, Automated; D010372:Pediatrics / Q000379:methods; D012307:Risk Factors; D012984:Software