Predicting postoperative liver cancer death outcomes with machine learning
Yong Wang1, Chaopeng Ji2,3, Ying Wang1
1Department of Anesthesiology, Pain and Perioperative Medicine, The first Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Objective:
To investigate the effect of 5 machine learning algorithms in predicting total hepatocellular carcinoma (HCC) postoperative death outcomes.
Methods:
This study was a secondary analysis. A prognosis model was established using machine learning with python.
Results:
The 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).
Conclusion:
Machine learning can predict total hepatocellular carcinoma postoperative death outcomes.
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