Feature-Level Insights into the Progesterone–Estradiol Ratio in Postmenopausal Women Using Explainable Machine Learning

Abstract

The protective role of progesterone against estradiol-driven proliferation is essential for preserving endometrial homeostasis. However, the factors that influence the progesterone–estradiol (P4:E2) ratio remain poorly characterized. This study aimed to model this ratio using a machine learning approach to identify key hormonal, anthropometric, demographic, dietary, metabolic, and inflammatory predictors. In addition, it aimed to assess estradiol and progesterone as individual outcomes to clarify whether shared or divergent mechanisms underlie variation in each hormone. NHANES data were used to identify postmenopausal women (n = 1902). An XGBoost model was developed to predict the log-transformed P4:E2 ratio using a 70/30 stratified train–test split. SHAP (SHapley Additive exPlanations) values were computed to interpret feature contributions. The final XGBoost model for the log-transformed P4:E2 ratio achieved an RMSE of 0.746, an MAE of 0.574, and an R2 of 0.298 on the test set. SHAP analysis identified FSH (0.213), waist circumference (0.181), and CRP (0.133) as the most influential contributors, followed by total cholesterol (0.085) and LH (0.066). FSH and waist circumference emerged as key predictors of estradiol, while total cholesterol and LH were the most influential for progesterone. By leveraging SHAP-based feature importance to rank predictors of the P4:E2 ratio, this study provides interpretable, data-driven insights into the reproductive hormonal dynamics of postmenopausal women.