GDM-BC: Non-invasive body composition dataset for intelligent prediction of Gestational Diabetes Mellitus

Gestational Diabetes Mellitus (GDM) refers to any degree of impaired glucose tolerance with onset or first recognition during pregnancy. As a high-prevalence disease, GDM damages the health of both pregnant women and fetuses in the short and long term. Accurate and cost-effective recognition of GDM is quite crucial to reduce the risk and economic pressure of this disease. However, existing datasets for the prediction of GDM primarily focus on clinical and biochemical parameters, including a mass of invasive indexes. These variables are hard to obtain and do not always perform well in the prediction of GDM. In this paper, we introduce a large-scale non-invasive body composition dataset, called GDM-BC, for intelligent risk prediction of GDM. Specifically, it contains a cohort of 39,438 pregnant women, of whom 7777 (19.7%) were subsequently diagnosed with GDM. Besides, our dataset includes a large number of body composition indexes that can be acquired non-invasively. In addition, we perform several traditional machine learning and deep learning methods on the GDM-BC dataset, among which the Residual Attention Fully Connected Network (RAFNet) performs the best, achieving an AUC (area under the ROC curve) of 0.920. The results show that our dataset is marvelous and creates a new perspective on the prediction of GDM. Our models may offer an opportunity to establish a cost-effective screening approach for identifying low-risk pregnant women based on body composition data. We believe that our proposed GDM-BC dataset will advance future research on risk prediction for GDM, as well as provide new insights for intelligent prediction of other high-incidence pregnancy-related diseases such as gestational hypertension.