Optimized machine learning model for predicting unplanned reoperation after rectal cancer anterior resection

Abstract

Background: Unplanned reoperation (URO) after surgery adversely affects the quality of life and prognosis of patients undergoing anterior resection for rectal cancer. This study aims to meet the urgent need for reliable predictive tools by developing an optimized machine learning model to estimate the risk of URO following anterior resection in rectal cancer patients.

Methods: This retrospective study collected multidimensional data from patients who underwent anterior resection for rectal cancer at Tongji Hospital of Huazhong University of Science and Technology from January 2012 to December 2022. Feature selection was conducted using both least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm. Multiple machine learning models were developed, with parameter optimization via grid search and cross-validation. Performance metrics included accuracy, specificity, sensitivity, and area under curve (AUC). The optimal model was interpreted using SHapley Additive exPlanations (SHAP), and an online platform was created for real-time risk prediction.

Results: A total of 2384 patients who underwent anterior resection for rectal cancer were included in this study. Following rigorous selection, 14 variables were identified for constructing the machine learning model. The optimized model demonstrated high predictive accuracy, with the random forest (RF) model achieving the best overall performance. The model achieved an AUC of 0.889 and an accuracy of 0.842 on the test dataset. SHAP analysis revealed that the tumor location, previous abdominal surgery, and operative time were the most significant factors influencing the risk of URO.

Conclusion: This study developed an optimized machine learning-based online predictive system to assess the risk of URO after anterior resection in rectal cancer patients. Accessible at https://yangsu2023.shinyapps.io/UROrisk/, this system improves prediction accuracy and offers real-time risk assessment, providing a valuable tool that may support clinical decision-making and potentially improve the prognosis of rectal cancer patients.

Keywords: Machine learning model; Rectal cancer; SHAP algorithms; Unplanned reoperation.