Comparative predictive performance of three machine learning algorithms for acute radiation enteritis risk among patients with cervical cancer undergoing radiotherapy: A prospective cohort study.

Objective: To develop a machine learning-based risk prediction model for acute radiation enteritis (ARE) in patients with cervical cancer, providing a new method for early and accurate prediction of ARE during radiotherapy. Methods: This prospective study enrolled patients with cervical cancer undergoing radiotherapy from March 2024 to March 2025. The patients were randomly divided into training and test sets at a 7:3 ratio. Prediction models were constructed using Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) algorithms. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, specificity, and F1-score. Results: The incidence of ARE was 52.85% (204/386). Among the three models, the Random Forest model demonstrated the best performance, with an AUC of 0.961, sensitivity of 0.934, and F1-score of 0.905. These performance metrics were consistently higher than those of the LR (AUC, 0.860; sensitivity, 0.739; F1-score, 0.736) and DT (AUC, 0.910; sensitivity, 0.887; F1-score, 0.873) models. The RF model showed good clinical utility in effectively identifying high-risk patients for early intervention. Feature importance ranking derived from the RF model identified the parametrial dose, radiotherapy time, clinical stage, rectal V40, age, Platelet-to-Lymphocyte Ratio (PLR), concurrent chemotherapy, and hypertension as the most influential predictors, in descending order of importance. Conclusions: The RF-based risk prediction model exhibited excellent performance in assessing the risk of ARE among patients with cervical cancer undergoing radiotherapy, thereby enabling individualized risk assessment and facilitating early preventive strategies.