article · Soft Computing
Gestational diabetes mellitus poses serious health risks to mothers and babies, making early detection preferable to standard diagnosis at 22 to 26 weeks of gestation. A three-layer architecture combining Internet of Things sensors, fog computing nodes, and cloud storage was developed to monitor maternal health. Connected sensors gather continuous vital signs, which are transferred to intermediate fog nodes for local processing and ultimately stored in the cloud. Within the fog layer, a dedicated methodology replaces unused cached data to preserve processing capacity for frequent incoming readings. Concurrently, a deep neural network predicts gestational diabetes risk in the second trimester, supported by Shapley additive explanations to deliver transparent local and global insights. Evaluated using data from 16,354 pregnant women within the MIMIC III benchmark dataset, the system achieved an accuracy of 0.957 and an area under the curve of 0.942.
Gestational diabetes is typically detected midway through pregnancy, but earlier warning allows for timely medical interventions that protect mothers and infants. Combining automated sensor monitoring with explainable artificial intelligence gives clinicians transparent risk predictions while avoiding heavy computational overhead through local fog processing, illustrating how connected healthcare architectures can enhance routine maternal care.
The framework could support clinical decision-support tools and remote patient-monitoring platforms operated by maternal healthcare providers. Utilizing cost-effective edge processing on fog nodes alongside Internet of Things sensors offers an efficient structure for healthcare software developers. However, the technology represents early-stage research, having been evaluated entirely on a retrospective benchmark dataset rather than through live clinical deployment.
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Abstract Gestational diabetes mellitus (GDM) is one of the pregnancy complications that endangers both mothers and babies. GDM is usually diagnosed at 22–26 weeks of gestation. However, early prediction is preferable because it may decrease the risk. The continuous monitoring of the mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this research is to provide a comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers, which are: (i) Internet of things (IoT) Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IoT sensors to aggregate vital signs from pregnancies using invasive and non-invasive sensors. The vital signs are then transmitted to fog nodes to be processed and finally stored in the cloud layer. The main contribution in this research is located in the fog layer producing the GDM module to implement two influential tasks which are as follows: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free up the cache space for new incoming data items. The cache replacement is very important in the case of the healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the occurrence of GDM in the second trimester of the pregnancy. To evaluate our model, we extracted data from 16,354 pregnant women from the medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data, and laboratory tests were aggregated. The results of the prediction model are superior to the state-of-the-art (ACC = 0.957, AUC = 0.942). Regarding explainability, we used Shapley additive explanation (SHAP) framework to provide local and global explanations for the developed models. Overall, the proposed framework is medically intuitive and allows the early prediction of GDM with a cost-effective solution.
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DOI: 10.1007/s00500-022-07420-1
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