Risk factors for apgar score using artificial neural networks

被引：0

作者：

Ibrahim, Doaa ^{[1
]}

Frize, Monique ^{[2
]}

Walker, Robin C. ^{[3
]}

机构：

[1] Univ Ottawa, Sch Informat Technol & Engn, Ottawa, ON, Canada

[2] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada

[3] Univ Ottawa, Fac Medy, Ottawa, ON K1N 6N5, Canada

来源：

2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15 | 2006年

关键词：

neural networks; perinatal outcomes; Apgar score;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页码：2126 / +

页数：2

共 14 条

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