Author:
Kang Byung Soo,Lee Seon Ui,Hong Subeen,Choi Sae Kyung,Shin Jae Eun,Wie Jeong Ha,Jo Yun Sung,Kim Yeon Hee,Kil Kicheol,Chung Yoo Hyun,Jung Kyunghoon,Hong Hanul,Park In Yang,Ko Hyun Sun
Abstract
AbstractThis study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks’ gestation), E1 (11–13 weeks’ gestation) and M1 (14–24 weeks’ gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original). A prediction model with the whole cohort achieved area under the receiver operating characteristics curve (AUC) and area under the precision-recall curve (AUPR) values of 0.711 and 0.246 at baseline, 0.720 and 0.256 at E0, 0.721 and 0.262 at E1, and 0.804 and 0.442 at M1, respectively. Then comparison of three models with different variable sets were performed: [a] variables from clinical guidelines; [b] selected variables from Shapley additive explanations (SHAP) values; and [c] Boruta algorithms. Based on model [c] with the least variables and similar or better performance than the other models, simple questionnaires were developed. The combined use of maternal factors and laboratory data could effectively predict individual risk of GDM using a machine learning model.
Funder
Korea Health Industry Development Institute
Publisher
Springer Science and Business Media LLC
Cited by
1 articles.
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1. Routine screening for gestational diabetes: a review;Current Opinion in Obstetrics & Gynecology;2024-01-16