Machine learning-based random forest predicts anastomotic leakage after anterior resection for rectal cancer

Abstract

Our findings suggest that eight factors may affect the incidence of AL. Our random forest classifier is an innovative and practical model to effectively predict AL, and could provide rational advice on whether to perform a temporary stoma, which might reduce the rate of stoma and avoid the ensuing complications.