Predicting Clinical Outcomes for Newborns Using Two Artificial Intelligence Approaches

Author

Frize, M. ; Ibrahim, D. ; Seker, H. ; Walker, R.C. ; Odetayo, M.O. ; Petrovic, D. ; Naguib, R.N.G.

Author_Institution

MIRG, Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada; MIRG, School of Information Technology and Engineering, University of Ottawa, Ottawa, Canada

Volume

2

fYear

2004

fDate

1-5 Sept. 2004

Firstpage

3202

Lastpage

3205

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.

Keywords

K-nearest neighbour algorithm; Neural Networks; fuzzy logic; fuzzy pattern classifiers; perinatal outcomes; Artificial intelligence; Biomedical computing; Biomedical measurements; Computational intelligence; Data structures; Hospitals; Information technology; Pediatrics; Systems engineering and theory; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE

Print_ISBN

0-7803-8439-3

Type

conf

DOI

10.1109/IEMBS.2004.1403902

Filename

1403902