A prediction model for successful vaginal birth after caesarean delivery in Chinese mothers by using machine learning
by Lin Rao; Wei Gao; Kaixin Fu; Yingying Zhang; Yu Huang; Xuan Zhou; Hong Li
International Journal of System Control and Information Processing (IJSCIP), Vol. 4, No. 2, 2024

Abstract: Background: This study aimed to build a personalised prediction algorithm for successful vaginal birth after caesarean delivery in Chinese mothers. Methods: This study used data from the electronic medical records of 406 admitted pregnant women between January 2010 and October 2020. Descriptive analyses, chi-square tests, and multivariate logistic regression were undertaken. By using the Spearman correlation coefficient, a prediction model for successful vaginal birth after caesarean delivery was derived. Results: The identified predictors included degree of cervical dilatation, fetal exposure, cervical canal regression, uterine orifice location, height, the thickness of the lower uterine segment, gestational age, BMI before delivery, and estimated birth weight. The prediction model performed well with an area under the receiver operating characteristics curve of 0.954 (95% CI, 0.87-0.94). Conclusion: The results show that the prediction model can better predict VBAC. The new prediction model may be used in clinical consultations to decide the preferred delivery mode.

Online publication date: Thu, 23-May-2024

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