Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms
Background: This study aimed to develop a clinical model to predict late-onset fetal growth restriction (FGR). Methods: This retrospective study included seven hospitals and was conducted between January 2009 and December 2020. Two sets of variables from the first trimester until 13 weeks (E1) and t...
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MDPI AG
2024-11-01
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| author | Seon Ui Lee Sae Kyung Choi Yun Sung Jo Jeong Ha Wie Jae Eun Shin Yeon Hee Kim Kicheol Kil Hyun Sun Ko |
| author_facet | Seon Ui Lee Sae Kyung Choi Yun Sung Jo Jeong Ha Wie Jae Eun Shin Yeon Hee Kim Kicheol Kil Hyun Sun Ko |
| author_sort | Seon Ui Lee |
| collection | DOAJ |
| description | Background: This study aimed to develop a clinical model to predict late-onset fetal growth restriction (FGR). Methods: This retrospective study included seven hospitals and was conducted between January 2009 and December 2020. Two sets of variables from the first trimester until 13 weeks (E1) and the early third trimester until 28 weeks (T1) were used to develop the FGR prediction models using a machine learning algorithm. The dataset was randomly divided into training and test sets (7:3 ratio). A simplified prediction model using variables with XGBoost’s embedded feature selection was developed and validated. Results: Precisely 32,301 patients met the eligibility criteria. In the prediction model for the whole cohort, the area under the curve (AUC) was 0.73 at E1 and 0.78 at T1 and the area under the precision-recall curve (AUPR) was 0.23 at E1 and 0.31 at T1 in the training set, while an AUC of 0.62 at E1 and 0.73 at T1 and an AUPR if 0.13 at E1, and 0.24 at T1 were obtained in the test set. The simplified prediction model performed similarly to the original model. Conclusions: A simplified machine learning model for predicting late FGR may be useful for evaluating individual risks in the early third trimester. |
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| institution | DOAJ |
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| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-eee22cce66ed4b2198a28a1c5f3b9f3e2025-08-20T02:48:05ZengMDPI AGLife2075-17292024-11-011411152110.3390/life14111521Prediction Model of Late Fetal Growth Restriction with Machine Learning AlgorithmsSeon Ui Lee0Sae Kyung Choi1Yun Sung Jo2Jeong Ha Wie3Jae Eun Shin4Yeon Hee Kim5Kicheol Kil6Hyun Sun Ko7Department of Obstetrics and Gynecology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaDepartment of Obstetrics and Gynecology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaDepartment of Obstetrics and Gynecology, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaDepartment of Obstetrics and Gynecology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaDepartment of Obstetrics and Gynecology, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaDepartment of Obstetrics and Gynecology, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaDepartment of Obstetrics and Gynecology, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaDepartment of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of KoreaBackground: This study aimed to develop a clinical model to predict late-onset fetal growth restriction (FGR). Methods: This retrospective study included seven hospitals and was conducted between January 2009 and December 2020. Two sets of variables from the first trimester until 13 weeks (E1) and the early third trimester until 28 weeks (T1) were used to develop the FGR prediction models using a machine learning algorithm. The dataset was randomly divided into training and test sets (7:3 ratio). A simplified prediction model using variables with XGBoost’s embedded feature selection was developed and validated. Results: Precisely 32,301 patients met the eligibility criteria. In the prediction model for the whole cohort, the area under the curve (AUC) was 0.73 at E1 and 0.78 at T1 and the area under the precision-recall curve (AUPR) was 0.23 at E1 and 0.31 at T1 in the training set, while an AUC of 0.62 at E1 and 0.73 at T1 and an AUPR if 0.13 at E1, and 0.24 at T1 were obtained in the test set. The simplified prediction model performed similarly to the original model. Conclusions: A simplified machine learning model for predicting late FGR may be useful for evaluating individual risks in the early third trimester.https://www.mdpi.com/2075-1729/14/11/1521fetal growth restrictiongestationmachine learning |
| spellingShingle | Seon Ui Lee Sae Kyung Choi Yun Sung Jo Jeong Ha Wie Jae Eun Shin Yeon Hee Kim Kicheol Kil Hyun Sun Ko Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms Life fetal growth restriction gestation machine learning |
| title | Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms |
| title_full | Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms |
| title_fullStr | Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms |
| title_full_unstemmed | Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms |
| title_short | Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms |
| title_sort | prediction model of late fetal growth restriction with machine learning algorithms |
| topic | fetal growth restriction gestation machine learning |
| url | https://www.mdpi.com/2075-1729/14/11/1521 |
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