• Yong Wang, Chaopeng Ji, Ying Wang, Muhuo Ji, Jian-Jun Yang, and Cheng-Mao Zhou.
• Department of Anesthesiology, Pain and Perioperative Medicine, The first Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
• Curr Med Res Opin. 2021 Apr 1; 37 (4): 629-634.

ObjectiveTo investigate the effect of 5 machine learning algorithms in predicting total hepatocellular carcinoma (HCC) postoperative death outcomes.MethodsThis study was a secondary analysis. A prognosis model was established using machine learning with python.ResultsThe results from the machine learning gbm algorithm showed that the most important factors, ranked from first to fifth, were: preoperative aspartate aminotransferase (GOT), preoperative AFP, preoperative cereal third transaminase (GPT), preoperative total bilirubin, and LC3. Postoperative death model results for liver cancer patients in the test group: of the 5 algorithm models, the highest accuracy rate was that of forest (0.739), followed by the gbm algorithm (0.714); of the 5 algorithms, the AUC values, from high to low, were forest (0.803), GradientBoosting (0.746), gbm (0.724), Logistic (0.660) and DecisionTree (0.578).ConclusionMachine learning can predict total hepatocellular carcinoma postoperative death outcomes.

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