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. 2020 Feb 25:8:e8583.
doi: 10.7717/peerj.8583. eCollection 2020.

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

Lei Lei #  1 , Ying Wang #  1 , Qiong Xue  1 , Jianhua Tong  1 , Cheng-Mao Zhou  1 , Jian-Jun Yang  1
Affiliations

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

Lei Lei et al. PeerJ. .

Abstract

Objective: 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.

Methods: 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.

Results: 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 being 0.333. The AUC values for the four algorithms were: Gbdt (0.772), gbm (0.725), forest (0.662) and DecisionTree (0.628).

Conclusions: Machine learning technology can predict acute kidney injury after hepatectomy. Age, cholesterol, tumor size, surgery duration and PLT influence the likelihood and development of postoperative acute kidney injury.

Keywords: AKI; Hepatectomy; Machine learning; Postoperative; Secondary analysis.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Correlation Analysis of various factors.
Figure 2
Figure 2. Variable importance of features included in Gbdt algorithm for prediction of AKI.
Figure 3
Figure 3. Machine learning algorithm for prediction of AKI in training group.
Figure 4
Figure 4. Machine learning algorithm for prediction of AKI in the testing group.
See this image and copyright information in PMC

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