A Prediction Model of Gestational Diabetes Mellitus Based on First Pregnancy Test Index - Details

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EI Scopus

Abstract：

The　purpose　of　this　study　is　to　discuss　the　possibility　of　predicting　gestational　diabetes　mellitus　(GDM)　by　analyzing　the　first　test　indexes.　In　order　to　verify　the　prediction　effect,　we　used　61　indexes,　including　age　and　60　test　indexes,　from　December　2015　to　May　2018　in　Beijing　Pinggu　District　Hospital,　and　conducted　experiments　of　GDM　risk　prediction　based　on　a　variety　of　different　models,　ranged　from　LR,　LDA,　RF　to　XGBoost.　The　experimental　results　reveal　that　compared　to　the　dataset　of　using　major　relevant　indicators,　the　dataset　of　using　full　indicators　performs　better.　Besides,　logistic　regression　can　achieve　a　relatively　good　prediction　effect.　On　the　test　set　of　all　data,　the　area　under　the　curve　(AUC)　of　the　Logistic　regression　model　reaches　0.7787.　In　the　meantime,　the　accuracy　rate　of　the　Logistic　Regression　model　reaches　(69.991　±　2.833)%,　and　the　recall　rate　and　the　mean　value　of　the　F1　value　are　(70.598　±　2.210)%　and　(70.264　±　2.128)%,　respectively.　So　the　analysis　based　on　the　first　pregnancy　test　can　play　a　role　in　predicting　GDM　to　a　certain　extent.　©　2020,　Springer　Nature　Switzerland　AG.

Keyword：

Forecasting Testing Obstetrics Logistic regression Predictive analytics

Author Community：

[ 1 ] [Yan, Jianzhuo]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Yan, Jianzhuo]Engineering Research Center of Digital Community, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Yan, Jianzhuo]Join Lab of Digital Health, Beijing University of Technology and Beijing Pinggu Hospital, Beijing; 100124, China
[ 4 ] [Geng, Yanan]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 5 ] [Geng, Yanan]Engineering Research Center of Digital Community, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Geng, Yanan]Join Lab of Digital Health, Beijing University of Technology and Beijing Pinggu Hospital, Beijing; 100124, China
[ 7 ] [Xu, Hongxia]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 8 ] [Xu, Hongxia]Engineering Research Center of Digital Community, Beijing University of Technology, Beijing; 100124, China
[ 9 ] [Xu, Hongxia]Join Lab of Digital Health, Beijing University of Technology and Beijing Pinggu Hospital, Beijing; 100124, China
[ 10 ] [Tan, Shaofeng]Join Lab of Digital Health, Beijing University of Technology and Beijing Pinggu Hospital, Beijing; 100124, China
[ 11 ] [Tan, Shaofeng]Information Center of Beijing Pinggu Hospital, Beijing; 101200, China
[ 12 ] [He, Dongdong]Join Lab of Digital Health, Beijing University of Technology and Beijing Pinggu Hospital, Beijing; 100124, China
[ 13 ] [He, Dongdong]Information Center of Beijing Pinggu Hospital, Beijing; 101200, China
[ 14 ] [Yu, Yongchuan]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 15 ] [Yu, Yongchuan]Engineering Research Center of Digital Community, Beijing University of Technology, Beijing; 100124, China
[ 16 ] [Deng, Sinuo]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 17 ] [Deng, Sinuo]Engineering Research Center of Digital Community, Beijing University of Technology, Beijing; 100124, China
[ 18 ] [Deng, Sinuo]Join Lab of Digital Health, Beijing University of Technology and Beijing Pinggu Hospital, Beijing; 100124, China
[ 19 ] [Du, Xiaoxue]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 20 ] [Du, Xiaoxue]Engineering Research Center of Digital Community, Beijing University of Technology, Beijing; 100124, China
[ 21 ] [Du, Xiaoxue]Join Lab of Digital Health, Beijing University of Technology and Beijing Pinggu Hospital, Beijing; 100124, China

Reprint Author's Address：

[xu, hongxia]join lab of digital health, beijing university of technology and beijing pinggu hospital, beijing; 100124, china;;[xu, hongxia]engineering research center of digital community, beijing university of technology, beijing; 100124, china;;[xu, hongxia]faculty of information technology, beijing university of technology, beijing; 100124, china

Email：

xhxccl@bjut.edu.cn

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Source ：

ISSN： 0302-9743

Year： 2020

Volume： 12435 LNCS

Page： 121-132

Language： English

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 2

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 1

Affiliated Colleges：

信息学部

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