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ML models were applied to different combinations of the parameters to predict SGA and severe SGA at birth (defined as 10th and third centile birth weight).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Using second\u2010trimester measurements, ML models achieved an accuracy of 70% and 73% in predicting SGA and severe SGA whereas clinical guidelines had accuracies of 64% and 48%. Uterine PI (Ut PI) was found to be an important predictor, corroborating with existing literature, but surprisingly, so was nuchal fold thickness (NF). Logistic regression showed that Ut PI and NF were significant predictors and statistical comparisons showed that these parameters were significantly different in disease. Further, including NF was found to improve ML model performance, and vice versa.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>ML could potentially improve the prediction of SGA at birth from second\u2010trimester measurements, and demonstrated reduced NF to be an important predictor. Early prediction of SGA allows closer clinical monitoring, which provides an opportunity to discover any underlying diseases associated with SGA.<\/jats:p><\/jats:sec>","DOI":"10.1002\/pd.5903","type":"journal-article","created":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T02:59:34Z","timestamp":1611025174000},"page":"505-516","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Machine learning improves early prediction of small\u2010for\u2010gestational\u2010age births and reveals nuchal fold thickness as unexpected predictor"],"prefix":"10.1002","volume":"41","author":[{"given":"Shier Nee","family":"Saw","sequence":"first","affiliation":[{"name":"Bioinformatics Institute Agency for Science Technology and Research (A*STAR)  Singapore"},{"name":"Department of Artificial Intelligence Faculty of Computer Science and Information Technology University of Malaya  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