Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network


Fetal distress is one of the main factors to cesarean section in obstetrics and gynecology. If the fetus lack of oxygen in uterus, threat to the fetal health and fetal death could happen. Cardiotocography (CTG) is the most widely used technique to monitor the fetal health and fetal heart rate (FHR) is an important index to identify occurs of fetal distress. This study is to propose discriminant analysis (DA), decision tree (DT), and artificial neural network (ANN) to evaluate fetal distress. The results show that the accuracies of DA, DT and ANN are 82.1%, 86.36% and 97.78%, respectively.

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Huang, M. and Hsu, Y. (2012) Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network. Journal of Biomedical Science and Engineering, 5, 526-533. doi: 10.4236/jbise.2012.59065.

Conflicts of Interest

The authors declare no conflicts of interest.


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