International Journal of Nursing Research

Objective To construct and verify the logistic regression model and BP neural network model of assisted reproduction technology (ART) patients, to evaluate and compare the two models, and to provide a new strategy for improving clinical pregnancy rate. Methods From January 2010 to May 2017, the clinical data of 13207 patients who had received ART treatments and transplanted embryos in the Reproduction Center of Medicine of a large grade A hospital in Hunan Province were retrospective analyzed. The influencing factors of clinical pregnancy were analyzed by univariate analysis and multivariate analysis. Logistic regression model and BP neural network model were constructed and verified. The receiver operating characteristic (ROC) curve was used to evaluate the model equation. Results In this study, a total of 12 predictive variables were entered into the final model, female age, the females level of education, No. of ART treatments, duration of infertility, endometrial thickness at the day of administration of hCG, therapeutic protocol, No. of embryos transplanted, quality of embryo transplanted, AFC, No. of oocytes retrieved, total dosage of Gn used and total duration of Gn used respectively. Compared with the two prediction models, BP neural network model had higher AUC (0.887), sensitivity (90.8%) and accuracy (83.5%), but it had lower specificity (49.6%). Logistic regression model had higher specificity (74.7%), but it had lower sensitivity (66.1%). Conclusion BP neural network model has high application value in predicting clinical pregnancy. It can be used in combination with logistic regression model to complement each other, contribute to achieve early prediction of clinical pregnancy, and provide basis and reference for further formulating individualized treatment plans, so as to improve the clinical pregnancy rate of ART patients.

BP neural network; Logistic regression; Assisted reproductive technology; Prediction model; Clinical pregnancy