Abstract
The incidence of multiple pregnancies has increased worldwide due to the use of assisted reproductive technologies, such as in vitro fertilization. In the USA, the rate of twin births has increased by nearly 80% since 1980, while the rate of triplet and higher-order births has increased by more than 400%. The overall rate of multiple births is around 3–4% of all pregnancies. Perinatal outcomes in multiple pregnancies depend on a variety of factors, including the number of fetuses, gestational age at delivery, and the health of the mother and babies. In recent decades, the multiple pregnancy rates and multiple live-birth rates have increased. Twin and multiple pregnancies are related to the increased risk of perinatal, morbidity, and mortality. The hospital-based perinatal mortality surveillance system has considered as research object in Zhejiang Province, including all the hospitals in 30 monitoring counties (districts), to collect the data for analyses. In our present study, all births (≥ 28 weeks of gestation) those born in the monitoring hospitals between 2008 and 2019 are included in the dataset. The historical data have been analyzed from 2008 to 2019 where the twin birth rate has been increased to 60.4%, while the triplet/ + birth rate has been decreased to 33.3%. In comparison with single births (7.5‰), the perinatal mortality rate within the 12 years has been increased remarkably higher in multiple births (22.0‰). In this article, on the basis of historical data, the machine learning-based forecasting models are devised, namely linear regression and SVM (support vector machine). The models show good accuracy in forecasting whether the pregnancy is singleton, multiple (normal), or multiple (abnormal). Machine learning methods cannot replace a medical practitioner but can offer a prediction model which can forecast the abnormalities in multiple pregnancies quickly for alerting a patient to get timely treatment. The accurate prediction models can save the lives of pregnant women by forecasting the abnormalities in the multiple pregnancies on time.
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The datasets generated and/or analyzed during the current study are not publicly available due [ethics and privacy].
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Acknowledgements
The authors thank the staff of maternal and child healthcare institutions and monitoring hospitals for their hard work in Zhejiang Province.
Funding
This work was supported by the National Science Foundation of China (No. 71804162), the Health Science and Technology program from Health Commission of Zhejiang Province (grant number 2019RC146), Soft Science Research Program from Zhejiang Provincial Department of Science and Technology (grant number 2022C35042) and the National Education Information Technology Research (186140084).
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LQ performed the project development, data collection and was a major contributor in writing the manuscript. WW analyzed the data. JJ and YW edited the manuscript text. All authors read and approved the final manuscript.
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This study was approved by the Medical Ethics Committee of Women’s hospital school of medicine Zhejiang University. All methods were performed in accordance with the relevant guidelines and regulations. Signed informed consent was obtained from all participants.
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Qian, L., Wu, W., Jiang, J. et al. The epidemiology of multiple pregnancy and perinatal outcome with the aid of machine learning-based forecasting models. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08745-1
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DOI: https://doi.org/10.1007/s00500-023-08745-1