Prediction model comparison for gestational diabetes mellitus with macrosomia based on risk factor investigation

Abstract

Purpose

To establish a feasible prediction model for gestational diabetes mellitus (GDM) with macrosomia based on risk factors analysis.

Methods

A total of 1981 GDM pregnant women with macrosomia were enrolled in this retrospective study. The potential risk factors were revealed between the GDM women with and without macrosomia based on questionnaire and clinical data analysis. Then, prediction models including logistic regression (LR), decision tree (DT), support vector machine (SVM) and artificial neural networks (ANN) were constructed using these risk factors. Effect evaluation was performed based on model forecasting ability and model practicability such as accuracy, true positive (TP) rate, false positive (FP) rate, recall, F-measure, and receiver operating characteristic curve (ROC).

Results

The risk factors analysis showed that factors such as triglyceride (TG), high-density lipoprotein-cholesterol (HDL-c) and ketone body were risk factors for GDM with macrosomia. Then, the forecasting model was constructed, respectively. Based on these risk factors as variables, the overall classification accuracy of the four forecasting models was 86%. DT model had the highest overall classification accuracy. SVM model had advantages over the other three models in terms of TP rate. Among the comparison parameters including overall ROC curve, ANN model was the highest, followed by LR model.

Conclusion

Among four forecasting models, ANN might be the optimal predication model, which had a certain practical value for the clinical screening of GDM women combined with macrosomia. Furthermore, HDL-c, TG, and ketone body might be potential risk factors for GDM with macrosomia.

Keywords:

• Gestational diabetes mellitus
• macrosomia
• prediction model
• risk factors

Acknowledgments

We deeply appreciate Jiangsu Overseas visiting scholar program for university prominent young and middle-aged teachers and presidents.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the “Six Big Talent Peak” Project of Jiangsu Province [grant number WSN-283]; the “333 Project” Science Research Project of Jiangsu Province [grant number BRA2016197]; Clinical Medicine Center Project of Nantong City [grant number HS2016005]; and Department of Science and Technology of Nantong [grant number MS12018052].