A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection

PeerJ
Lei LeiJian-Jun Yang

Abstract

Machine learning methods may have better or comparable predictive ability than traditional analysis. We explore machine learning methods to predict the likelihood of acute kidney injury after liver cancer resection. This is a secondary analysis cohort study. We reviewed data from patients who had undergone resection of primary hepatocellular carcinoma between January 2008 and October 2015. The analysis included 1,173 hepatectomy patients, 77 (6.6%) of whom had AKI and 1,096 (93.4%) who did not. The importance matrix for the Gbdt algorithm model shows that age, cholesterol, tumor size, surgery duration and PLT were the five most important parameters. Figure 1 shows that Age, tumor size and surgery duration had weak positive correlations with AKI. Cholesterol and PLT also had weak negative correlations with AKI. The models constructed by the four machine learning algorithms in the training group were compared. Among the four machine learning algorithms, random forest and gbm had the highest accuracy, 0.989 and 0.970 respectively. The precision of four of the five algorithms was 1, random forest being the exception. Among the test group, gbm had the highest accuracy (0.932). Random forest and gbm had the highest precision, both be...Continue Reading

References

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Citations

Apr 17, 2020·Journal of Clinical Medicine·Charat ThongprayoonWisit Cheungpasitporn
Jun 11, 2020·Journal of Clinical Medicine·Charat ThongprayoonWisit Cheungpasitporn
Sep 17, 2021·World Journal of Surgical Oncology·Maciej KrasnodębskiMichał Grąt

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Software Mentioned

DecisionTree
Scikit
gbm
pandas
Gbdt
Python
learn
profiling
forest
Python Software Foundation

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