Machine learning predicts duodenal stump leakage risk in gastric cancer

A new predictive model utilizing machine learning has shown promise in forecasting duodenal stump leakage after laparoscopic gastrectomy for gastric cancer. Analyzing data from 4,070 patients, the support vector machine emerged as the most effective algorithm, achieving high sensitivity, accuracy, and specificity. Key factors influencing leakage risk include tumor location, tumor stage, operation time, preoperative pyloric obstruction, and patient age. This model not only aids in risk assessment but also supports preventive strategies in surgical care.