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

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 A

SUBMITTER: Lei L 

PROVIDER: S-EPMC7047869 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature