{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T14:30:39Z","timestamp":1791469839116,"version":"4.3.4"},"reference-count":21,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J Gynecology &amp;amp; Obste"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the predictive potential of first\u2010trimester biomarkers\u2014pregnancy\u2010associated plasma protein\u2010A (PAPP\u2010A) and free \u03b2\u2010human chorionic gonadotropin (\u03b2\u2010hCG)\u2014combined with maternal body mass index (BMI), using machine learning (ML) algorithms for the early detection of gestational diabetes mellitus (GDM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective cohort study was conducted with 400 pregnant women who underwent first\u2010trimester screening at Ankara Bilkent City Hospital. Demographic, clinical, and biochemical data, including PAPP\u2010A, free \u03b2\u2010hCG, and BMI, were collected. ML models, including random forest, gradient boosting, and logistic regression, were employed to predict GDM risk. Data standardization, model training, and performance evaluation were performed using metrics such as accuracy, F1 score, and receiver operating characteristics area under the curve (ROC\u2010AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The combination of PAPP\u2010A, free \u03b2\u2010hCG, and BMI significantly enhanced GDM prediction accuracy across all ML models. Gradient boosting achieved the highest performance with an ROC\u2010AUC of 0.715 and an accuracy of 71.3%, demonstrating the robust predictive value of these variables. PAPP\u2010A alone showed limited predictive capacity (ROC\u2010AUC: 0.632), but its integration with BMI improved model performance substantially. Cut\u2010off analyses identified key thresholds for PAPP\u2010A (&lt;1.02) and BMI (&gt;26.12) for effective risk stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study underscores the potential of integrating first\u2010trimester biomarkers with ML algorithms for early GDM prediction. By using routinely collected clinical data, this approach offers a cost\u2010effective and scalable solution for improving maternal and neonatal health outcomes. Future research should validate these findings in diverse populations and explore the incorporation of additional biomarkers to further refine predictive models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ijgo.70264","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T07:05:23Z","timestamp":1748847923000},"page":"1189-1196","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Determining the risk of gestational diabetes using machine learning: A study on first\u2010trimester PAPP\u2010A and \u03b2\u2010hCG data"],"prefix":"10.1002","volume":"171","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8912-0812","authenticated-orcid":false,"given":"U\u011furcan","family":"Zorlu","sequence":"first","affiliation":[{"name":"Department of Perinatalogy Ankara Bilkent City Hospital  Ankara Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7977-4364","authenticated-orcid":false,"given":"Burak","family":"Elmas","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Ankara Bilkent City Hospital  Ankara Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1064-8727","authenticated-orcid":false,"given":"Gonca T\u00fcrker","family":"Erg\u00fcn","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Ankara Bilkent City Hospital  Ankara Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9187-2941","authenticated-orcid":false,"given":"Serap Topkara","family":"Sucu","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Ankara Etlik City Hospital  Ankara Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0761-911X","authenticated-orcid":false,"given":"Elif","family":"Ozan","sequence":"additional","affiliation":[{"name":"Departmant of Obstetric and Gynecology Private Koru Ankara Hospital  Ankara Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9453-9178","authenticated-orcid":false,"given":"Ece","family":"Aydo\u011fdu","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Eskisehir City Hospital  Eski\u015fehir Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8567-9048","authenticated-orcid":false,"given":"Dilek","family":"\u015eahin","sequence":"additional","affiliation":[{"name":"Department of Perinatalogy Ankara Bilkent City Hospital  Ankara Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8167-3837","authenticated-orcid":false,"given":"\u00d6zlem Moralo\u011flu","family":"Tekin","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Ankara Bilkent City Hospital  Ankara Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"key":"e_1_2_9_2_1","volume-title":"IDF Diabetes Atlas","author":"International Diabetes Federation","year":"2021"},{"key":"e_1_2_9_3_1","unstructured":"T\u00fcrkiye Klinikleri.Gestasyonel diyabetin maternal komplikasyonlar\u0131. 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