{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T12:42:58Z","timestamp":1787316178713,"version":"build-2736575974"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T00:00:00Z","timestamp":1580428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: The objective of this study was to investigate the use of ensemble methods to improve the prediction of fetal macrosomia and large for gestational age from prenatal ultrasound imaging measurements. Methods: We evaluated and compared the prediction accuracies of nonlinear and quadratic mixed-effects models coupled with 26 different empirical formulas for estimating fetal weights in predicting large fetuses at birth. The data for the investigation were taken from the Successive Small-for-Gestational-Age-Births study. Ensemble methods, a class of machine learning techniques, were used to improve the prediction accuracies by combining the individual models and empirical formulas. Results: The prediction accuracy of individual statistical models and empirical formulas varied considerably in predicting macrosomia but varied less in predicting large for gestational age. Two ensemble methods, voting and stacking, with model selection, can combine the strengths of individual models and formulas and can improve the prediction accuracy. Conclusions: Ensemble learning can improve the prediction of fetal macrosomia and large for gestational age and have the potential to assist obstetricians in clinical decisions.<\/jats:p>","DOI":"10.3390\/jcm9020380","type":"journal-article","created":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T11:55:56Z","timestamp":1580471756000},"page":"380","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Ensemble Learning to Improve the Prediction of Fetal Macrosomia and Large-for-Gestational Age"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7504-8215","authenticated-orcid":false,"given":"Shangyuan","family":"Ye","sequence":"first","affiliation":[{"name":"Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6825-5649","authenticated-orcid":false,"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, Department of Prevention Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyan","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Public Health and Management, Weifang Medical University, Weifang, Shandong 261053, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Public Health, Peking University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzhen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Public Health and Management, Weifang Medical University, Weifang, Shandong 261053, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1574-198X","authenticated-orcid":false,"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children\u2019s Hospital and Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1016\/j.ajog.2009.02.035","article-title":"The association between birthweight 4000 g or greater and perinatal outcomes in patients with and without gestational diabetes mellitus","volume":"200","author":"Esakoff","year":"2009","journal-title":"Am. 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