Identifying the key influencing factors of psychological birth trauma in primiparous women with interpretable machine learning.

Objective: Accurately identifying the key influencing factors of psychological birth trauma in primiparous women is crucial for implementing effective preventive and intervention measures. This study aimed to develop and validate an interpretable machine learning prediction model for identifying the key influencing factors of psychological birth trauma in primiparous women. Methods: A multicenter cross-sectional study was conducted on primiparous women in four tertiary hospitals in Sichuan Province, southwestern China, from December 2023 to March 2024. The Childbirth Trauma Index was used in assessing psychological birth trauma in primiparous women. Data were collected and randomly divided into a training set (80 %, Results: Among the six machine learning models, the Multilayer Perceptron Regression model exhibited the best overall performance in the testing set (MAE = 3.977, MSE = 24.832, Conclusions: Interpretable machine learning prediction models can identify the key influencing factors of psychological birth trauma in primiparous women. SHAP and ALE analyses based on the Multilayer Perceptron Regression model can help healthcare providers understand the complex decision-making logic within a prediction model. This study provides a scientific basis for the early prevention and personalized intervention of psychological birth trauma in primiparous women.