Predicting Clinical Outcomes for Newborns Using Two Artificial Intelligence Approaches

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

    Research output: Chapter in Book/Report/Conference proceedingChapter

    10 Citations (Scopus)

    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.
    Original languageEnglish
    Title of host publicationEngineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
    PublisherIEEE
    Pages3202 - 3205
    Volume2
    ISBN (Print)0-7803-8439-3
    DOIs
    Publication statusPublished - 2004
    Event26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - San Francisco, United States
    Duration: 1 Sep 20044 Sep 2004

    Conference

    Conference26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    Abbreviated titleIEEE-EMB International Annual Meeting
    CountryUnited States
    CitySan Francisco
    Period1/09/044/09/04

    Fingerprint

    Fuzzy Logic
    Artificial Intelligence
    Databases
    Apgar Score
    Infant Mortality

    Bibliographical note

    This paper is not available on the repository

    Cite this

    Frize, M., Ibrahim, D., Seker, H., Walker, R. C., Odetayo, M. O., Petrovic, D., & Naguib, R. N. G. (2004). Predicting Clinical Outcomes for Newborns Using Two Artificial Intelligence Approaches. In Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE (Vol. 2, pp. 3202 - 3205). IEEE. https://doi.org/10.1109/IEMBS.2004.1403902

    Predicting Clinical Outcomes for Newborns Using Two Artificial Intelligence Approaches. / Frize, M.; Ibrahim, D.; Seker, H.; Walker, R.C.; Odetayo, M.O.; Petrovic, Dobrila; Naguib, R.N.G.

    Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE. Vol. 2 IEEE, 2004. p. 3202 - 3205.

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Frize, M, Ibrahim, D, Seker, H, Walker, RC, Odetayo, MO, Petrovic, D & Naguib, RNG 2004, Predicting Clinical Outcomes for Newborns Using Two Artificial Intelligence Approaches. in Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE. vol. 2, IEEE, pp. 3202 - 3205, 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, United States, 1/09/04. https://doi.org/10.1109/IEMBS.2004.1403902
    Frize M, Ibrahim D, Seker H, Walker RC, Odetayo MO, Petrovic D et al. Predicting Clinical Outcomes for Newborns Using Two Artificial Intelligence Approaches. In Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE. Vol. 2. IEEE. 2004. p. 3202 - 3205 https://doi.org/10.1109/IEMBS.2004.1403902
    Frize, M. ; Ibrahim, D. ; Seker, H. ; Walker, R.C. ; Odetayo, M.O. ; Petrovic, Dobrila ; Naguib, R.N.G. / Predicting Clinical Outcomes for Newborns Using Two Artificial Intelligence Approaches. Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE. Vol. 2 IEEE, 2004. pp. 3202 - 3205
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