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J Korean Soc Matern Child Health > Volume 22(3); 2018 > Article
Journal of The Korean Society of Maternal and Child Health 2018;22(3):151-161.
DOI: https://doi.org/10.21896/jksmch.2018.22.3.151    Published online September 30, 2018.
Prediction of Gestational Age at Birth using an Artificial Neural Networks in Singleton Preterm Birth
Jee Yun Lee1, Soo Jung Jo1, Eun Jin Jung1, Kwang Sig Lee2, Seung Woo Kim3, Ho Yeon Kim1, Geum Joon Cho1, Soon Cheol Hong1, Min Jeong Oh1, Hai Joong Kim1, Ki Hoon Ahn1
1Department of Obstetrics & Gynecology, Korea University College of Medicine, Korea
2AI Center, Anam Hospital, Korea University College of Medicine, Korea
3Artificial Intelligence Research Team, RiskSolutions, Korea
인공신경망을 이용한 조산 단태아의 분만 임신주수 예측
이지윤1, 조수정1, 정은진1, 이광식2, 김승우3, 김호연1, 조금준1, 홍순철1, 오민정1, 김해중1, 안기훈1
1고려대학교 의과대학 산부인과교실
2고려대학교 안암병원 AI센터
3리스크솔루션 인공지능 연구팀
Correspondence:  Ki Hoon Ahn,
Email: akh1220@hanmail.net
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.
Key Words: preterm delivery, artificial neural networks
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