Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery

Cheng-Mao Zhou; HuiJuan Li; Qiong Xue; Jian-Jun Yang; Yu Zhu

doi:10.1016/j.heliyon.2024.e26580

Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery

Heliyon. 2024 Feb 22;10(5):e26580. doi: 10.1016/j.heliyon.2024.e26580. eCollection 2024 Mar 15.

Authors

Cheng-Mao Zhou¹, HuiJuan Li², Qiong Xue², Jian-Jun Yang², Yu Zhu¹

Affiliations

¹ Big Data and Artificial Intelligence Research Group, Department of Anaesthesiology and Nursing, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China.
² Big Data and Artificial Intelligence Research Group, Department of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.

Abstract

Objective: By constructing a predictive model using machine learning and deep learning technologies, we aim to understand the risk factors for postoperative intestinal obstruction in laparoscopic colorectal cancer patients, and establish an effective artificial intelligence-based predictive model to guide individualized prevention and treatment, thus improving patient outcomes.

Methods: We constructed a model of the artificial intelligence algorithm in Python. Subjects were randomly assigned to either a training set for variable identification and model construction, or a test set for testing model performance, at a ratio of 7:3. The model was trained with ten algorithms. We used the AUC values of the ROC curves, as well as accuracy, precision, recall rate and F1 scores.

Results: The results of feature engineering composited with the GBDT algorithm showed that opioid use, anesthesia duration, and body weight were the top three factors in the development of POI. We used ten machine learning and deep learning algorithms to validate the model, and the results were as follows: the three algorithms with best accuracy were XGB (0.807), Decision Tree (0.807) and Neural DecisionTree (0.807); the two algorithms with best precision were XGB (0.500) and Decision Tree (0.500); the two algorithms with best recall rate were adab (0.243) and Decision Tree (0.135); the two algorithms with highest F1 score were adab (0.290) and Decision Tree (0.213); and the three algorithms with best AUC were Gradient Boosting (0.678), XGB (0.638) and LinearSVC (0.633).

Conclusion: This study shows that XGB and Decision Tree are the two best algorithms for predicting the risk of developing ileus after laparoscopic colon cancer surgery. It provides new insight and approaches to the field of postoperative intestinal obstruction in colorectal cancer through the application of machine learning techniques, thereby improving our understanding of the disease and offering strong support for clinical decision-making.

Keywords: AUC; Deep learning; Machine learning; Opioids; POI; Prediction; XGB.