Early gestational diabetes mellitus risk predictor using neural network with NearMiss
Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective preven...
Saved in:
| Published in: | Gynecological endocrinology Vol. 41; no. 1; p. 2470317 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Taylor & Francis Group
01.12.2025
|
| Subjects: | |
| ISSN: | 0951-3590, 1473-0766, 1473-0766 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective prevention and timely intervention during the initial stages.
The primary objective of this study is to pinpoint potential risk factors for GDM and develop an early GDM risk prediction model using neural networks to facilitate GDM screening in early pregnancy.
Initially, we employed statistical tests and models, including univariate and multivariate logistic regression, to identify 14 potential risk factors. Subsequently, we applied various resampling techniques alongside a multi-layer perceptron (MLP). Finally, we evaluated and compared the classification performances of the constructed models using various metric indicators.
As a result, we identified several factors in early pregnancy significantly associated with GDM (
< 0.05), including BMI, age of menarche, age, higher education, folic acid supplementation, family history of diabetes mellitus, HGB, WBC, PLT, Scr, HBsAg, ALT, ALB, and TBIL. Employing the multivariate logistic model as the baseline achieved an accuracy and AUC of 0.777. In comparison, the MLP-based model using NearMiss exhibited strong predictive performance, achieving scores of 0.943 in AUC and 0.884 in accuracy.
In this study, we proposed an innovative interpretable early GDM risk prediction model based on MLP. This model is designed to offer assistance in estimating the risk of GDM in early pregnancy, enabling proactive prevention and timely intervention. |
|---|---|
| AbstractList | Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective prevention and timely intervention during the initial stages.
The primary objective of this study is to pinpoint potential risk factors for GDM and develop an early GDM risk prediction model using neural networks to facilitate GDM screening in early pregnancy.
Initially, we employed statistical tests and models, including univariate and multivariate logistic regression, to identify 14 potential risk factors. Subsequently, we applied various resampling techniques alongside a multi-layer perceptron (MLP). Finally, we evaluated and compared the classification performances of the constructed models using various metric indicators.
As a result, we identified several factors in early pregnancy significantly associated with GDM (
< 0.05), including BMI, age of menarche, age, higher education, folic acid supplementation, family history of diabetes mellitus, HGB, WBC, PLT, Scr, HBsAg, ALT, ALB, and TBIL. Employing the multivariate logistic model as the baseline achieved an accuracy and AUC of 0.777. In comparison, the MLP-based model using NearMiss exhibited strong predictive performance, achieving scores of 0.943 in AUC and 0.884 in accuracy.
In this study, we proposed an innovative interpretable early GDM risk prediction model based on MLP. This model is designed to offer assistance in estimating the risk of GDM in early pregnancy, enabling proactive prevention and timely intervention. Background Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective prevention and timely intervention during the initial stages.Objective The primary objective of this study is to pinpoint potential risk factors for GDM and develop an early GDM risk prediction model using neural networks to facilitate GDM screening in early pregnancy.Methods Initially, we employed statistical tests and models, including univariate and multivariate logistic regression, to identify 14 potential risk factors. Subsequently, we applied various resampling techniques alongside a multi-layer perceptron (MLP). Finally, we evaluated and compared the classification performances of the constructed models using various metric indicators.Results As a result, we identified several factors in early pregnancy significantly associated with GDM (p < 0.05), including BMI, age of menarche, age, higher education, folic acid supplementation, family history of diabetes mellitus, HGB, WBC, PLT, Scr, HBsAg, ALT, ALB, and TBIL. Employing the multivariate logistic model as the baseline achieved an accuracy and AUC of 0.777. In comparison, the MLP-based model using NearMiss exhibited strong predictive performance, achieving scores of 0.943 in AUC and 0.884 in accuracy.Conclusions In this study, we proposed an innovative interpretable early GDM risk prediction model based on MLP. This model is designed to offer assistance in estimating the risk of GDM in early pregnancy, enabling proactive prevention and timely intervention. Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective prevention and timely intervention during the initial stages.BACKGROUNDGestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective prevention and timely intervention during the initial stages.The primary objective of this study is to pinpoint potential risk factors for GDM and develop an early GDM risk prediction model using neural networks to facilitate GDM screening in early pregnancy.OBJECTIVEThe primary objective of this study is to pinpoint potential risk factors for GDM and develop an early GDM risk prediction model using neural networks to facilitate GDM screening in early pregnancy.Initially, we employed statistical tests and models, including univariate and multivariate logistic regression, to identify 14 potential risk factors. Subsequently, we applied various resampling techniques alongside a multi-layer perceptron (MLP). Finally, we evaluated and compared the classification performances of the constructed models using various metric indicators.METHODSInitially, we employed statistical tests and models, including univariate and multivariate logistic regression, to identify 14 potential risk factors. Subsequently, we applied various resampling techniques alongside a multi-layer perceptron (MLP). Finally, we evaluated and compared the classification performances of the constructed models using various metric indicators.As a result, we identified several factors in early pregnancy significantly associated with GDM (p < 0.05), including BMI, age of menarche, age, higher education, folic acid supplementation, family history of diabetes mellitus, HGB, WBC, PLT, Scr, HBsAg, ALT, ALB, and TBIL. Employing the multivariate logistic model as the baseline achieved an accuracy and AUC of 0.777. In comparison, the MLP-based model using NearMiss exhibited strong predictive performance, achieving scores of 0.943 in AUC and 0.884 in accuracy.RESULTSAs a result, we identified several factors in early pregnancy significantly associated with GDM (p < 0.05), including BMI, age of menarche, age, higher education, folic acid supplementation, family history of diabetes mellitus, HGB, WBC, PLT, Scr, HBsAg, ALT, ALB, and TBIL. Employing the multivariate logistic model as the baseline achieved an accuracy and AUC of 0.777. In comparison, the MLP-based model using NearMiss exhibited strong predictive performance, achieving scores of 0.943 in AUC and 0.884 in accuracy.In this study, we proposed an innovative interpretable early GDM risk prediction model based on MLP. This model is designed to offer assistance in estimating the risk of GDM in early pregnancy, enabling proactive prevention and timely intervention.CONCLUSIONSIn this study, we proposed an innovative interpretable early GDM risk prediction model based on MLP. This model is designed to offer assistance in estimating the risk of GDM in early pregnancy, enabling proactive prevention and timely intervention. |
| Author | Zhao, Min Su, Xiaojie Huang, Lihong |
| Author_xml | – sequence: 1 givenname: Min surname: Zhao fullname: Zhao, Min – sequence: 2 givenname: Xiaojie surname: Su fullname: Su, Xiaojie – sequence: 3 givenname: Lihong surname: Huang fullname: Huang, Lihong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39992231$$D View this record in MEDLINE/PubMed |
| BookMark | eNo9kU1v1DAURS1URKeFnwDykk2G5-94iaoWKhXY0LXl2C-D20w82Imq_nsSZtqVn6yje6V7LsjZmEck5CODLYMWvoBVTCgLWw5cbbk0IJh5QzZMGtGA0fqMbFamWaFzclHrAwAT0vB35FxYazkXbEPur30ZnukO6-SnlEc_0Jh8hxNWusdhSNNcaUn1kR4KxhSmXOhc07ijI85loUecnnJ5pE9p-kN_oi8_Uq3vydveDxU_nN5Lcn9z_fvqe3P369vt1de7JggDUyNVx02USmrLeoi9BY26DzaYNgJ2cblYx21QPQsoeeAoeq9AtdihN7oVl-T2mBuzf3CHkva-PLvsk_v_kcvO-TKlMKCLArn2YLUMSiLIVmsrDfNrkZQdLlmfj1mHkv_Oyx5un2pYJvAj5rm6ZV7gphWw1n46oXO3x_ha_DLrAqgjEEqutWD_ijBwqz73os-t-txJn_gHFoONAg |
| Cites_doi | 10.2337/db09-1371 10.1084/jem.20081188 10.2337/dc09-1393 10.1109/CIET.2018.8660844 10.1007/s10064-022-02664-5 10.1186/s12884-019-2519-9 10.2337/diacare.26.7.2005 10.1371/journal.pone.0036727 10.2337/dc07-2345 10.1007/s00125-016-4062-9 10.1007/s00404-017-4505-7 10.1161/CIR.0b013e31820faaf8 10.1007/s00125-011-2150-4 10.1056/NEJMoa0707943 10.1177/1054773818769210 10.1016/j.ijmedinf.2023.105228 10.1038/ijo.2017.277 10.1186/s12933-016-0338-0 10.1016/j.diabres.2009.04.025 10.2337/diacare.24.4.659 10.1136/bmj-2022-070244 10.3389/fendo.2017.00144 10.1136/bmjopen-2017-016972 10.2337/dc06-2361 10.2337/dc11-0135 10.1016/j.diabet.2020.02.003 10.2337/dc21-1430 10.1093/humrep/15.8.1826 10.1016/j.diabres.2021.109001 10.1038/s41591-019-0724-8 10.1002/pd.2636 10.1093/oxfordjournals.aje.a116408 10.1016/j.ejogrb.2009.04.016 10.6133/apjcn.201906_28(2).0014 10.1007/978-1-4614-5441-0_4 10.1186/s13006-019-0227-8 10.2337/dc10-1766 10.1007/s00394-021-02749-z 10.1186/s40842-024-00176-7 10.1016/S0140-6736(09)60731-5 10.2337/dc08-0706 10.1007/s00125-016-3985-5 10.1177/1049732304270825 10.3109/09513590.2013.871522 10.1016/j.diabres.2018.02.023 10.2337/dc16-2397 10.3390/ijerph16224511 10.1136/bmj.m1361 |
| ContentType | Journal Article |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 DOA |
| DOI | 10.1080/09513590.2025.2470317 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic DOAJ Open Access Full Text |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1473-0766 |
| ExternalDocumentID | oai_doaj_org_article_d3e26a0964c54e048669471ad0eb44be 39992231 10_1080_09513590_2025_2470317 |
| Genre | Journal Article |
| GroupedDBID | --- 00X 03L 0YH 29I 36B 4.4 53G 5GY AAFWJ AALUX AAYXX ABBKH ABDBF ABEIZ ABLKL ABUPF ABWVI ABXYU ACENM ACGEJ ACGFO ACGFS ACUHS ADCVX ADRBQ ADXPE AECIN AEGXH AENEX AEOZL AFKVX AFPKN AFRVT AGDLA AGYJP AHMBA AIAGR AIJEM AJWEG AKBVH ALMA_UNASSIGNED_HOLDINGS ALQZU ALYBC AQTUD BABNJ BLEHA BOHLJ CCCUG CITATION CS3 DKSSO DU5 EAP EBC EBD EBS EMB EMK EMOBN EPL ESX F5P GROUPED_DOAJ H13 HZ~ KRBQP KSSTO KWAYT KYCEM LGLTD LJTGL M4Z O9- P2P RRB RWL SV3 TAE TDBHL TFDNU TFL TFW TUROJ TUS UEQFS V1S ~1N 5VS 7X7 88E 8AO 8FI 8FJ AALIY AAMIU AAORF AAPUL AAPXX AAQRR ABJNI ABLIJ ABUWG ABWCV ABZEW ACKZS ACOPL ADBBV ADFOM ADFZZ ADYSH AEIIZ AFKRA AFLEI AGFJD AGRBW AJVHN ALIPV AMDAE AWYRJ BENPR BPHCQ BRMBE BVXVI CAG CCPQU CGR COF CUY CVF CYYVM CZDIS DRXRE DWTOO ECM EIF EJD FYUFA HMCUK IPNFZ JENTW M1P M44 NPM NUSFT PHGZM PHGZT PQQKQ PROAC PSQYO QQXMO RIG RNANH RVRKI S0X TBQAZ TERGH UKHRP 7X8 |
| ID | FETCH-LOGICAL-c370t-45b27d454691f0df906e6fc9c78d0ebdc9c1b29c5f1ce42c2e3fa5058ebea7683 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001432476900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0951-3590 1473-0766 |
| IngestDate | Fri Oct 03 12:43:56 EDT 2025 Sun Nov 09 09:37:30 EST 2025 Sun May 11 01:41:37 EDT 2025 Sat Nov 29 08:21:34 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Gestational diabetes mellitus logistic regression NearMiss neural network algorithms |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c370t-45b27d454691f0df906e6fc9c78d0ebdc9c1b29c5f1ce42c2e3fa5058ebea7683 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://doaj.org/article/d3e26a0964c54e048669471ad0eb44be |
| PMID | 39992231 |
| PQID | 3170278308 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_d3e26a0964c54e048669471ad0eb44be proquest_miscellaneous_3170278308 pubmed_primary_39992231 crossref_primary_10_1080_09513590_2025_2470317 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-Dec |
| PublicationDateYYYYMMDD | 2025-12-01 |
| PublicationDate_xml | – month: 12 year: 2025 text: 2025-Dec |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Gynecological endocrinology |
| PublicationTitleAlternate | Gynecol Endocrinol |
| PublicationYear | 2025 |
| Publisher | Taylor & Francis Group |
| Publisher_xml | – name: Taylor & Francis Group |
| References | e_1_3_3_50_1 e_1_3_3_18_1 e_1_3_3_39_1 e_1_3_3_14_1 e_1_3_3_37_1 e_1_3_3_16_1 e_1_3_3_35_1 e_1_3_3_10_1 e_1_3_3_33_1 e_1_3_3_12_1 e_1_3_3_31_1 e_1_3_3_40_1 (e_1_3_3_6_1) 2021 e_1_3_3_7_1 e_1_3_3_9_1 e_1_3_3_29_1 e_1_3_3_25_1 e_1_3_3_48_1 e_1_3_3_27_1 e_1_3_3_46_1 e_1_3_3_3_1 e_1_3_3_21_1 e_1_3_3_44_1 e_1_3_3_5_1 e_1_3_3_23_1 e_1_3_3_42_1 e_1_3_3_30_1 e_1_3_3_17_1 e_1_3_3_19_1 e_1_3_3_13_1 e_1_3_3_38_1 e_1_3_3_15_1 e_1_3_3_36_1 e_1_3_3_34_1 e_1_3_3_11_1 e_1_3_3_32_1 e_1_3_3_41_1 e_1_3_3_8_1 e_1_3_3_28_1 e_1_3_3_24_1 e_1_3_3_49_1 e_1_3_3_26_1 e_1_3_3_47_1 e_1_3_3_2_1 e_1_3_3_20_1 e_1_3_3_45_1 e_1_3_3_4_1 e_1_3_3_22_1 e_1_3_3_43_1 |
| References_xml | – ident: e_1_3_3_8_1 doi: 10.2337/db09-1371 – ident: e_1_3_3_37_1 doi: 10.1084/jem.20081188 – volume-title: IDF DIABETES ATLAS year: 2021 ident: e_1_3_3_6_1 – ident: e_1_3_3_36_1 doi: 10.2337/dc09-1393 – ident: e_1_3_3_46_1 doi: 10.1109/CIET.2018.8660844 – ident: e_1_3_3_47_1 doi: 10.1007/s10064-022-02664-5 – ident: e_1_3_3_13_1 doi: 10.1186/s12884-019-2519-9 – ident: e_1_3_3_25_1 doi: 10.2337/diacare.26.7.2005 – ident: e_1_3_3_34_1 doi: 10.1371/journal.pone.0036727 – ident: e_1_3_3_7_1 doi: 10.2337/dc07-2345 – ident: e_1_3_3_33_1 doi: 10.1007/s00125-016-4062-9 – ident: e_1_3_3_12_1 doi: 10.1007/s00404-017-4505-7 – ident: e_1_3_3_22_1 doi: 10.1161/CIR.0b013e31820faaf8 – ident: e_1_3_3_28_1 doi: 10.1007/s00125-011-2150-4 – ident: e_1_3_3_11_1 doi: 10.1056/NEJMoa0707943 – ident: e_1_3_3_4_1 doi: 10.1177/1054773818769210 – ident: e_1_3_3_44_1 doi: 10.1016/j.ijmedinf.2023.105228 – ident: e_1_3_3_30_1 doi: 10.1038/ijo.2017.277 – ident: e_1_3_3_19_1 doi: 10.1186/s12933-016-0338-0 – ident: e_1_3_3_39_1 doi: 10.1016/j.diabres.2009.04.025 – ident: e_1_3_3_26_1 doi: 10.2337/diacare.24.4.659 – ident: e_1_3_3_17_1 doi: 10.1136/bmj-2022-070244 – ident: e_1_3_3_18_1 doi: 10.3389/fendo.2017.00144 – ident: e_1_3_3_3_1 doi: 10.1136/bmjopen-2017-016972 – ident: e_1_3_3_38_1 doi: 10.2337/dc06-2361 – ident: e_1_3_3_29_1 doi: 10.2337/dc11-0135 – ident: e_1_3_3_27_1 doi: 10.1016/j.diabet.2020.02.003 – ident: e_1_3_3_15_1 doi: 10.2337/dc21-1430 – ident: e_1_3_3_10_1 doi: 10.1093/humrep/15.8.1826 – ident: e_1_3_3_43_1 doi: 10.1016/j.diabres.2021.109001 – ident: e_1_3_3_42_1 doi: 10.1038/s41591-019-0724-8 – ident: e_1_3_3_41_1 doi: 10.1002/pd.2636 – ident: e_1_3_3_2_1 doi: 10.1093/oxfordjournals.aje.a116408 – ident: e_1_3_3_40_1 doi: 10.1016/j.ejogrb.2009.04.016 – ident: e_1_3_3_49_1 doi: 10.6133/apjcn.201906_28(2).0014 – ident: e_1_3_3_21_1 doi: 10.1007/978-1-4614-5441-0_4 – ident: e_1_3_3_14_1 doi: 10.1186/s13006-019-0227-8 – ident: e_1_3_3_35_1 doi: 10.2337/dc10-1766 – ident: e_1_3_3_50_1 doi: 10.1007/s00394-021-02749-z – ident: e_1_3_3_45_1 doi: 10.1186/s40842-024-00176-7 – ident: e_1_3_3_23_1 doi: 10.1016/S0140-6736(09)60731-5 – ident: e_1_3_3_24_1 doi: 10.2337/dc08-0706 – ident: e_1_3_3_32_1 doi: 10.1007/s00125-016-3985-5 – ident: e_1_3_3_9_1 doi: 10.1177/1049732304270825 – ident: e_1_3_3_20_1 doi: 10.3109/09513590.2013.871522 – ident: e_1_3_3_5_1 doi: 10.1016/j.diabres.2018.02.023 – ident: e_1_3_3_31_1 doi: 10.2337/dc16-2397 – ident: e_1_3_3_48_1 doi: 10.3390/ijerph16224511 – ident: e_1_3_3_16_1 doi: 10.1136/bmj.m1361 |
| SSID | ssj0013472 |
| Score | 2.42139 |
| Snippet | Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers... Background Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for... |
| SourceID | doaj proquest pubmed crossref |
| SourceType | Open Website Aggregation Database Index Database |
| StartPage | 2470317 |
| SubjectTerms | Adult Diabetes, Gestational - diagnosis Diabetes, Gestational - epidemiology Female Gestational diabetes mellitus Glucose Tolerance Test Humans logistic regression NearMiss neural network algorithms Neural Networks, Computer Pregnancy Risk Assessment - methods Risk Factors |
| Title | Early gestational diabetes mellitus risk predictor using neural network with NearMiss |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39992231 https://www.proquest.com/docview/3170278308 https://doaj.org/article/d3e26a0964c54e048669471ad0eb44be |
| Volume | 41 |
| WOSCitedRecordID | wos001432476900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1473-0766 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0013472 issn: 0951-3590 databaseCode: DOA dateStart: 20230101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVAWR databaseName: Taylor & Francis Online Journals customDbUrl: eissn: 1473-0766 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0013472 issn: 0951-3590 databaseCode: TFW dateStart: 19870101 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis – providerCode: PRVAWR databaseName: Taylor & Francis Open Access customDbUrl: eissn: 1473-0766 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0013472 issn: 0951-3590 databaseCode: 0YH dateStart: 20231201 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1JS8UwEB5URLyIu8-NCF6rbZamOar48OLDg-K7lWwVD9bHW_z9znQRPYgXL6WEpElnkuabzPQbgHMbCuOEdokvnEukUjyxXFVJiktJ5EZqnfsm2YQejYrx2Dx8S_VFMWEtPXAruMsgIs8tAm3plYxEEEdPyGxIo5PSRfr6ptr0xlTvP5BN2qYmibxQJu3_3SFWbSyjIrQNubrgkgjc9Y9dqSHv_x1xNjvPcBM2OsjIrtqhbsFSrLdh7b5ziu_AU0NSzMhR1J3ssf5Elb0R3-Z8MWMUQs4mU2qEVjajcPcXRmSWWLtuQ8EZncmyEU79e9TGLjwNbx9v7pIuW0LihU7nKGfHdZAK7d2sSkNl0jzmlTdeFySrgHeZ48arKvNRcs-jqCzinwLVaNHoEHuwUr_X8QBYkBxxhZOiCLlUFhvZCmGiiTp3lVdhABe9tMpJS4pRZj3XaCfeksRbduIdwDXJ9KsycVo3BajpstN0-ZemB3DWa6TENUCODVvH98WsxB7IgSrSYgD7raq-ukIAZhACZYf_MYQjWKfXasNZjmFlPl3EE1j1H_PX2fQUlvW4OG0mIl4fh8-f3HveQg |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Early+gestational+diabetes+mellitus+risk+predictor+using+neural+network+with+NearMiss&rft.jtitle=Gynecological+endocrinology&rft.au=Zhao%2C+Min&rft.au=Su%2C+Xiaojie&rft.au=Huang%2C+Lihong&rft.date=2025-12-01&rft.issn=1473-0766&rft.eissn=1473-0766&rft.volume=41&rft.issue=1&rft.spage=2470317&rft_id=info:doi/10.1080%2F09513590.2025.2470317&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0951-3590&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0951-3590&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0951-3590&client=summon |