Interpretable machine learning model for identification and risk factor of premature rupture of membranes (PROM) and its association with nutritional inflammatory index: a retrospective study.

BACKGROUND

Premature rupture of membranes (PROM) poses significant risks to both maternal and neonatal health. This study aims to construct a risk factor prediction model related to PROM by using machine learning technology and explore the association with nutritional inflammatory index.

METHODS

A retrospective analysis was conducted on patients with PROM. Based on the variables screened out by ridge regression and Boruta algorithm, univariate and multivariate logistic regression analyses were further adopted. According to the sample data, it is divided into the training set and the internal validation set in a ratio of 7:3. The research group adopted four machine learning algorithms: Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). The selected variables were incorporated into model construction, with the area under the receiver operating characteristic (ROC) curve (AUC) serving as a criterion for model selection. Model performance was assessed using AUC values, sensitivity, specificity, recall, F1 score, and accuracy. The variables were selected based on the contribution degree of the variables in Shapley additive Interpretation (SHAP) to construct the nomogram.

RESULTS

A retrospective analysis was conducted involving 800 parturients at Binhai County People's Hospital from January 2023 to October 2024, comprising 400 with PROM and 400 with normal delivery. The RF model demonstrated superior performance with an AUC of 0.757, sensitivity of 67.4%, and specificity of 65.1%. Key predictive factors identified included body mass index (BMI), prognostic nutritional index (PNI), platelet, albumin, and aggregate index of systemic inflammation (AISI). The ROC of the model also showed good efficacy, with an AUC of 0.777.

CONCLUSION

This study highlights the potential of machine learning in enhancing the understanding and prediction of PROM, and emphasizes the significance of inflammatory and nutritional indicators, paving the way for future research in maternal-fetal medicine.