Pregnancy and postpartum period are the most obvious stages of physiological changes in pregnant women, during which pregnant women are more likely to suffer from cardiovascular diseases. Traditional evaluation methods have certain limitations, therefore a more efficient and accurate evaluation method is needed. This article aims to explore the feasibility of using deep learning technology to analyze physiological data of pregnant women for risk assessment of cardiovascular diseases during pregnancy and postpartum period. Deep learning technology can explore and mine features and patterns related to cardiovascular disease in pregnant women, improving evaluation accuracy. This article explores the application effects of deep learning technology from three aspects: model selection, feature extraction, and data enhancement, and conducts a series of experiments. The experimental results indicate that a recurrent neural network should be selected as the training model, and convolutional neural network methods should be used for feature extraction. By combining appropriate data augmentation techniques, the accuracy of maternal cardiovascular disease risk assessment can be improved.

Jiajing Zhang. Using Deep Learning Technology to Analyze Physiological Data of Pregnant Women for Risk Assessment of Cardiovascular Diseases during Pregnancy and Postpartum Period. International Journal of Frontiers in Medicine (2023), Vol. 5, Issue 8: 60-67. https://doi.org/10.25236/IJFM.2023.050808.

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