What question did this study set out to answer?

This research aims to develop a machine learning model to predict the risk of recurrence after endometrial polypectomy using multiple biomarkers.

April 1, 2026Open Access

Prediction of Recurrence After Endometrial Polypectomy in Women of Childbearing Age Based on Machine Learning Algorithm

Key Points

This research aims to develop a machine learning model to predict the risk of recurrence after endometrial polypectomy using multiple biomarkers.
Analyzed a retrospective cohort of 606 patients who underwent endometrial polypectomy.
Extracted variables such as age, BMI, reproductive history, and polyp characteristics.
Trained seven machine learning algorithms including random forest and logistic regression with 10-fold cross-validation.
Evaluated model performance using metrics like AUC, accuracy, sensitivity, specificity, PPV, NPV, and F1 score.
29.5% of patients developed recurrence within one year after surgery.
Random forest algorithm showed the best performance in predicting recurrence with an AUC of 0.838 in the training set.
In the validation set, random forest maintained a strong performance with an AUC of 0.760.
Key predictors of recurrence included age, polyp location, and hemoglobin level as identified by SHAP analysis.

Abstract

Objective: Research on machine learning (ML) prediction models for recurrence after endometrial polypectomy remains unexplored. In this study, we aim to establish an ML-driven model based on multi-biomarkers to predict recurrence after endometrial polypectomy. Methods: A retrospective cohort of 606 patients who underwent endometrial polypectomy was analyzed. Variables including age, BMI, reproductive history, prior uterine surgery, coexisting uterine disorders, polyp characteristics, and routine blood indices were extracted. Seven ML algorithms (logistic regression, SVM, MLP, random forest, KNN, Naïve Bayes, and decision tree) were trained using 10-fold cross-validation. Performance was evaluated by AUC, accuracy, sensitivity, specificity, PPV, NPV, and F1 score. Results: Of the 606 patients, 179 (29.5%) developed recurrence within one year postoperatively. The cohort was randomly divided into a training set (n = 424) and a validation set (n = 182). In the training set, the random forest (RF) algorithm achieved the best performance (AUC = 0.838, accuracy = 79.5%, specificity = 0.930, F1 score = 0.576). In the validation set, RF remained superior (AUC = 0.760, accuracy = 75.3%, specificity = 0.875, F1 score = 0.526), underscoring its strong generalizability. SHAP analysis identified age, posterior-wall polyp location, prior uterine surgery, histopathological subtype, and hemoglobin level as the most influential predictors of recurrence. Conclusion: The RF-based model, using demographic, clinical, and hematologic features, showed high accuracy in predicting recurrence risk after endometrial polypectomy. This interpretable ML framework can help clinicians identify high-risk patients early and personalize postoperative surveillance. Keywords: machine learning, endometrial polypectomy, prediction model, recurrence

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