title: Predicting clinical outcomes for newborns using two artificial intelligence approaches.
abstract: Two different approaches, based on artificial neural networks (ANN) and fuzzy logic, were used to predict a number of outcomes of newborns: How they would be delivered, their 5 minute Apgar score, and neonatal mortality. The goal was to assess whether the methods would be comparable or whether they would perform differently for different outcomes. The results were comparable for Correct Classification Rate (CCR) and Specificity (true negative cases). Sensitivity (true positive cases) was slightly higher for the back-propagation feed-forward ANN than using the Fuzzy-Logic Classifier (FLC). Since this is one single database and a very large one, it is possible that the FLC would perform better than the ANN for very small databases, as shown by some of the co-authors in the past. The next step will be to test a small database with both methods to assess strengths and weaknesses with the intent to use both if needed with some medical data in the future.
authors.affiliation: MIRG, Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada.
authors.forename: M
authors.initials: M
authors.lastname: Frize
authors.forename: D
authors.initials: D
authors.lastname: Ibrahim
authors.forename: H
authors.initials: H
authors.lastname: Seker
authors.forename: R C
authors.initials: RC
authors.lastname: Walker
authors.forename: M O
authors.initials: MO
authors.lastname: Odetayo
authors.forename: D
authors.initials: D
authors.lastname: Petrovic
authors.forename: R N G
authors.initials: RN
authors.lastname: Naguib