ArticleBioinformation2026
Machine learning based prediction of gestational diabetes mellitus using early pregnancy biomarkers and clinical data.
Article in Bioinformation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Gestational diabetes mellitus (GDM) is a common pregnancy-related condition that can lead to significant maternal and neonatal complications, but conventional screening methods often delay diagnosis. Therefore, it is of interest to develop a machine learning model for early prediction of GDM using first-trimester biomarkers and clinical data. Hence, a prospective study of 100 pregnant women was conducted and various machine learning algorithms were trained to predict GDM. The random forest model showed the best performance with an accuracy of 86% and an AUC of 0.90. Thus, we show the potential of machine learning in enabling early prediction and timely intervention for GDM, improving maternal and neonatal outcomes.
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