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Electronic health records (EHRs) promise GDM risk prediction, but missing data poses a challenge to developing reliable and generalizable risk prediction models. This study aims to address the problem of missing EHR data in GDM prediction before 12 weeks gestation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 5066 women with singleton pregnancies, aged 18 to 50, were included in this retrospective study. This study evaluated 6 imputation methods, combined with 4 classification machine learning models. The evaluation encompassed downstream predictive performance, robustness to variable missingness, ability to restore original data distribution, and influence on feature selection based on 10-fold cross-validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our findings revealed a significant improvement in model performance with imputation. When using the top 30 features, logistic regression (LR) with multivariate imputation by chained equations using classification and regression trees (mice) achieved the highest area under the receiver operating characteristic curve of 0.6899, compared to 0.6336 for the LR model without imputation. Mice also led to the best average performance across prediction models and yielded the most accurate restoration of the original data distribution. LR models trained on data imputed by mice remained the most robust across varying levels of missingness. The classification algorithm primarily accounted for differences in predictive performance. In addition, we identified 18 key features for early GDM prediction in the Chinese population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates the critical role of imputation in improving the performance and fairness of GDM prediction models. The findings provide practical guidance for integrating imputation into clinical machine learning pipelines.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076251352436","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T06:44:28Z","timestamp":1753857868000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing early gestational diabetes mellitus prediction with imputation-based machine learning framework: A comparative study on real-world clinical records"],"prefix":"10.1177","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2645-6754","authenticated-orcid":false,"given":"Leyao","family":"Ma","sequence":"first","affiliation":[{"name":"Institute of Medical Information, Chinese Academy of Medical Science &amp; Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9211-0079","authenticated-orcid":false,"given":"Lin","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Medical Information, Chinese Academy of Medical Science &amp; 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