J Korean Soc Matern Child Health.  2018 Sep;22(3):151-161. 10.21896/jksmch.2018.22.3.151.

Prediction of Gestational Age at Birth using an Artificial Neural Networks in Singleton Preterm Birth

• Lee JY
• Jo SJ
• Jung EJ
• Lee KS
• Kim SW
• Kim HY
• Cho GJ
• Hong SC
• Oh MJ
• Kim HJ
• Ahn KH

• KMID: 2421913
• DOI: http://doi.org/10.21896/jksmch.2018.22.3.151

Abstract

PURPOSE
The objective of the present study was to predict the gestational age at preterm birth using artificial neural networks for singleton pregnancy.
METHODS
Artificial neural networks (ANNs) were used as a tool for the prediction of gestational age at birth. ANNs trained using obstetrical data of 125 cases, including 56 preterm and 69 non-preterm deliveries. Using a 36-variable obstetrical input set, gestational weeks at delivery were predicted by 89 cases of training sets, 18 cases of validating sets, and 18 cases of testing sets (total: 125 cases). After training, we validated the model by another 12 cases containing data of preterm deliveries.
RESULTS
To define the accuracy of the developed model, we confirmed the correlation coefficient (R) and mean square error of the model. For validating sets, the correlation coefficient was 0.839, but R of testing sets was 0.892, and R of total 125 cases was 0.959. The neural networks were well trained, and the model predictions were relatively good. Furthermore, the model was validated with another dataset of 12 cases, and the correlation coefficient was 0.709. The error days were 11.58±13.73.
CONCLUSION
In the present study, we trained the ANNs and developed the predictive model for gestational age at delivery. Although the prediction for gestational age at birth in singleton preterm birth was feasible, further studies with larger data, including detailed risk variables of preterm birth and other obstetrical outcomes, are needed.

Keyword

preterm delivery; artificial neural networks

MeSH Terms

Dataset
Gestational Age*
Parturition*
Pregnancy
Premature Birth*