1. Clinical data-based modeling of IVF live birth outcome and its application
- Author
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Liu Liu, Hua Liang, Jing Yang, Fujin Shen, Jiao Chen, and Liangfei Ao
- Subjects
IVF ,Machine learning ,Live birth outcome ,Embryo transfer strategy, clinical decision support ,Gynecology and obstetrics ,RG1-991 ,Reproduction ,QH471-489 - Abstract
Abstract Background The low live birth rate and difficult decision-making of the in vitro fertilization (IVF) treatment regimen bring great trouble to patients and clinicians. Based on the retrospective clinical data of patients undergoing the IVF cycle, this study aims to establish classification models for predicting live birth outcome (LBO) with machine learning methods. Methods The historical data of a total of 1405 patients undergoing IVF cycle were first collected and then analyzed by univariate and multivariate analysis. The statistically significant factors were identified and taken as input to build the artificial neural network (ANN) model and supporting vector machine (SVM) model for predicting the LBO. By comparing the model performance, the one with better results was selected as the final prediction model and applied in real clinical applications. Results Univariate and multivariate analysis shows that 7 factors were closely related to the LBO (with P
- Published
- 2024
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