Volume 24 Issue 1
Jan.  2026
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Article Navigation >  Chinese Journal of General Practice  > 2026  >  24(1): 92-95
LIN Yangyang, HUANG Yingying, CAO Xiaodan, LI Xiaoqing, ZHANG Na. Research on risk prediction model of spontaneous premature delivery in pregnant women with diabetes mellitus based on machine learning[J]. Chinese Journal of General Practice, 2026, 24(1): 92-95. doi: 10.16766/j.cnki.issn.1674-4152.004337
Citation: LIN Yangyang, HUANG Yingying, CAO Xiaodan, LI Xiaoqing, ZHANG Na. Research on risk prediction model of spontaneous premature delivery in pregnant women with diabetes mellitus based on machine learning[J]. Chinese Journal of General Practice, 2026, 24(1): 92-95. doi: 10.16766/j.cnki.issn.1674-4152.004337
shu

Research on risk prediction model of spontaneous premature delivery in pregnant women with diabetes mellitus based on machine learning

doi: 10.16766/j.cnki.issn.1674-4152.004337
  • 1.

    Nursing Teaching and Research Office of Zhejiang Dongfang Polytechnic, Wenzhou, Zhejiang 325000, China

Funds:

 LBY23H200008

  • Received Date: 2025-07-18
    Available Online: 2026-04-01
    Abstract
  •   Objective  To construct the risk prediction model of spontaneous preterm birth (SPB) in pregnant women with diabetes mellitus (GDM) based on the four machine learning algorithms, and to screen the optimal model to provide a reference for identifying the SPB population of GDM pregnant women.  Methods  The case data of 221 pregnant women with GDM who gave birth in the Obstetrics and Gynecology Department of Wenzhou People' s Hospital from January 2023 to January 2025 were collected. They were divided into a premature birth group (68 cases) and a normal group (153 cases) based on whether they were premature. Multivariate logistic analysis was used to identify the risk factors for SPB in GDM pregnant women. The patients were randomly divided into a 70% training set (155 cases) and a 30% validation set (66 cases). Four machine learning algorithms, namely decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were used to construct a GDM pregnant woman SPB risk prediction model, and the optimal model was evaluated and screened.  Results  Multivariate logistic analysis showed that age≤35 years old, BMI≥24, Hypertension during pregnancy, family history of diabetes, guided infection during pregnancy, and elevated WBC count in the third trimester were independent risk factors for SPB in GDM pregnant women, while good blood glucose control was a protective factor for SPB in GDM pregnant women. The AUC value of the RF model was 0.925, the accuracy was 0.863, the precision was 0.887, the sensitivity was 0.729, the specificity was 0.975, and the F1 score was 0.826, all of which are superior to the other three models.  Conclusion  A GDM pregnant women' s SPB risk prediction model is constructed based on four machine learning algorithms: decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). RF prediction performs the best and can effectively screen high-risk populations for SPB in GDM pregnant women, providing support for clinical intervention plan formulation.

     

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