Multivariate machine learning models for prediction of postoperative intestinal obstruction in patients underwent laparoscopic colorectal surgery: A retrospective observational study

Abstract

Background Machine learning may predict postoperative intestinal obstruction (POI) in patients underwent laparoscopic colorectal surgery for malignant lesions.Methods We used five machine learning algorithms (Logistic regression, Decision Tree, Forest, Gradient Boosting and gbm), analyzed by 28 explanatory variables, to predict POI. The total samples were randomly divided into training and testing groups, with a ratio of 8:2. The model was evaluated by the area operation characteristic curve (AUC), F1-Measure, accuracy, recall, and MSE under the receiver.Results A total of 637 patients were enrolled in this study, 122 (19.15%) of them had POI. Gradient Boosting and gbm had the most accurate in training group and testing group respectively.The f1_score of Gradient Boosting was the highest in the training group (f1_score =0.710526), and the f1_score of gbm was the highest in the testing group (f1_score =0.500000). In addition, the results of the importance matrix of Gbdt algorithm model showed that the important variables that account for the weight of intestinal obstruction after the first five operations are time to pass flatus or passage of stool, cumulative dose of rescue opioids used in postoperative days 3 (POD 3), duration of surgery, height and weight.Conclusions Machine learning algorithms may predict the occurrence of POI in patients underwent laparoscopic colorectal surgery for malignant lesions, especially Gradient Boosting and GBM algorithms. Moreover, time to pass flatus or passage of stool, cumulative dose of rescue opioids used during POD 3, duration of surgery, height and weight play an important role in the development of POI.