Predicting postoperative liver cancer death outcomes with machine learning

Abstract

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.

Keywords:

• Machine learning
• hepatocellular carcinoma
• mortality
• postoperative
• AUC

Transparency

Declaration of funding

This study was supported by grants from the National Natural Science Foundation of China [Nos., 81600950, 81771156, 81772126].

Declaration of financial/other relationships

The authors have no financial/other relationships to declare. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

All authors contributed to data analysis, drafting and revising the article, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

Acknowledgements

We are grateful to the BioStudies (public) database for including and providing Professor Chih-Wen Lin’s original data²⁶.

Data availability statement

Data is available from the BioStudies database (https://www.ebi.ac.uk/biostudies/studies?query=S-EPMC6122804), accession number: S-EPMC6122804.

Ethical approval

This study was a secondary analysis. The data is available from the BioStudies database (https://www.ebi.ac.uk/biostudies/studies?query=S-EPMC6122804), accession number: S-EPMC6122804. Since this is a secondary analysis using a public database, our ethics committee (First Affiliated Hospital of Zhengzhou University) has exempted us from ethical examination and approval.

Consent for publication

All personal data consent was obtained from the corresponding individual.