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

doi:10.1109/IEMBS.2004.1403902

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 proceeding › Chapter

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 language	English
Title of host publication	Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
Publisher	IEEE
Pages	3202 - 3205
Volume	2
ISBN (Print)	0-7803-8439-3
DOIs	https://doi.org/10.1109/IEMBS.2004.1403902
Publication status	Published - 2004
Event	26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - San Francisco, United States Duration: 1 Sept 2004 → 4 Sept 2004

Conference

Conference	26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated title	IEEE-EMB International Annual Meeting
Country/Territory	United States
City	San Francisco
Period	1/09/04 → 4/09/04

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This paper is not available on the repository

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10.1109/IEMBS.2004.1403902

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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

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

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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.",

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

note = "This paper is not available on the repository; 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE-EMB International Annual Meeting ; Conference date: 01-09-2004 Through 04-09-2004",

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AU - Frize, M.

AU - Ibrahim, D.

AU - Seker, H.

AU - Walker, R.C.

AU - Odetayo, M.O.

AU - Petrovic, Dobrila

AU - Naguib, R.N.G.

N1 - This paper is not available on the repository

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AB - 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.

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

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T2 - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 1 September 2004 through 4 September 2004

ER -

Predicting Clinical Outcomes for Newborns Using Two Artificial Intelligence Approaches

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