PMID- 34954978
OWN - NLM
STAT- MEDLINE
DCOM- 20211228
LR  - 20211228
IS  - 0254-6450 (Print)
IS  - 0254-6450 (Linking)
VI  - 42
IP  - 12
DP  - 2021 Dec 10
TI  - [Development and evaluation of a machine learning prediction model for large for 
      gestational age].
PG  - 2143-2148
LID - 10.3760/cma.j.cn112338-20210824-00677 [doi]
AB  - Objective: To develop and validate a useful predictive model for large 
      gestational age (LGA) in pregnancy using a machine learning (ML) algorithm and 
      compare its performance with the traditional logistic regression model. Methods: 
      Data were obtained from the National Free Preconception Health Examination 
      Project in China, carried out in 220 counties of 31 provinces from 2010 to 2012, 
      covering all rural couples with a planned pregnancy. This study included all 
      teams of childbearing age who delivered newborns within 24-42 weeks of 
      gestational age and their newborns. Ten different ML algorithms were used to 
      establish LGA prediction models, and the prediction performance of these models 
      was evaluated. Results: A total of 104 936 newborns were included, including 54 
      856 boys (52.3%) and 50 080 girls (47.7%). The incidence of LGA was 11.7% (12 
      279). The imbalance between the two groups was addressed by the under- sampling 
      technique, after which the overall performance of the ML models was significantly 
      improved. The CatBoost model achieved the highest area under the 
      receiver-operating-characteristic curve (AUC) value of 0.932. The logistic 
      regression model had the worst performance, with an AUC of 0.555. Conclusions: In 
      predicting the risk for LGA in pregnancy, the ML algorithms outperform the 
      traditional logistic regression method. Compared to other ML algorithms, CatBoost 
      could improve the performance, and it deserves further investigation.
FAU - Bai, X
AU  - Bai X
AD  - Department of Endocrinology, Key Laboratory of Endocrinology of National Health 
      Commission/State Key Laboratory of Complex Severe and Rare Diseases/Peking Union 
      Medical College Hospital/Chinese Academy of Medical Science and Peking Union 
      Medical College, Beijing 100730, China.
FAU - Luo, Y Y
AU  - Luo YY
AD  - Department of Endocrinology, Key Laboratory of Endocrinology of National Health 
      Commission/State Key Laboratory of Complex Severe and Rare Diseases/Peking Union 
      Medical College Hospital/Chinese Academy of Medical Science and Peking Union 
      Medical College, Beijing 100730, China.
FAU - Zhou, Z B
AU  - Zhou ZB
AD  - Department of Endocrinology, Key Laboratory of Endocrinology of National Health 
      Commission/State Key Laboratory of Complex Severe and Rare Diseases/Peking Union 
      Medical College Hospital/Chinese Academy of Medical Science and Peking Union 
      Medical College, Beijing 100730, China.
FAU - Su, M L
AU  - Su ML
AD  - DHC Mediway Technology Co., Ltd, Beijing 100190, China.
FAU - Yang, L Q
AU  - Yang LQ
AD  - DHC Mediway Technology Co., Ltd, Beijing 100190, China.
FAU - Chen, S
AU  - Chen S
AD  - Department of Endocrinology, Key Laboratory of Endocrinology of National Health 
      Commission/State Key Laboratory of Complex Severe and Rare Diseases/Peking Union 
      Medical College Hospital/Chinese Academy of Medical Science and Peking Union 
      Medical College, Beijing 100730, China.
FAU - Yang, H B
AU  - Yang HB
AD  - Department of Endocrinology, Key Laboratory of Endocrinology of National Health 
      Commission/State Key Laboratory of Complex Severe and Rare Diseases/Peking Union 
      Medical College Hospital/Chinese Academy of Medical Science and Peking Union 
      Medical College, Beijing 100730, China.
FAU - Zhu, H J
AU  - Zhu HJ
AD  - Department of Endocrinology, Key Laboratory of Endocrinology of National Health 
      Commission/State Key Laboratory of Complex Severe and Rare Diseases/Peking Union 
      Medical College Hospital/Chinese Academy of Medical Science and Peking Union 
      Medical College, Beijing 100730, China.
FAU - Pan, H
AU  - Pan H
AD  - Department of Endocrinology, Key Laboratory of Endocrinology of National Health 
      Commission/State Key Laboratory of Complex Severe and Rare Diseases/Peking Union 
      Medical College Hospital/Chinese Academy of Medical Science and Peking Union 
      Medical College, Beijing 100730, China.
LA  - chi
PT  - Journal Article
PL  - China
TA  - Zhonghua Liu Xing Bing Xue Za Zhi
JT  - Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
JID - 8208604
SB  - IM
MH  - *Algorithms
MH  - Female
MH  - Gestational Age
MH  - Humans
MH  - Infant
MH  - Infant, Newborn
MH  - Logistic Models
MH  - *Machine Learning
MH  - Male
MH  - Pregnancy
MH  - ROC Curve
EDAT- 2021/12/27 06:00
MHDA- 2021/12/29 06:00
CRDT- 2021/12/26 21:44
PHST- 2021/12/26 21:44 [entrez]
PHST- 2021/12/27 06:00 [pubmed]
PHST- 2021/12/29 06:00 [medline]
AID - 10.3760/cma.j.cn112338-20210824-00677 [doi]
PST - ppublish
SO  - Zhonghua Liu Xing Bing Xue Za Zhi. 2021 Dec 10;42(12):2143-2148. doi: 
      10.3760/cma.j.cn112338-20210824-00677.