Precision prevention of colorectal neoplasia in patients undergoing hemorrhoid surgery: an explainable machine learning model for identifying the risk of precancerous neoplastic polyps.

Least absolute shrinkage and selection operator identified age, sex, BMI, smoking, drinking, and hypertension as key predictors.

Among the evaluated models, RF achieved the highest training-set AUROC (0.892).

In the testing set, the AUROC was 0.738 with a Brier score of 0.172, suggesting acceptable calibration and reasonable overall stability.

SHAP analysis showed that age contributed most to the predictions, followed by drinking, BMI, smoking, sex, and hypertension.