Cargando…
A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection
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...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7047869/ https://www.ncbi.nlm.nih.gov/pubmed/32140301 http://dx.doi.org/10.7717/peerj.8583 |
_version_ | 1783502195583352832 |
---|---|
author | Lei, Lei Wang, Ying Xue, Qiong Tong, Jianhua Zhou, Cheng-Mao Yang, Jian-Jun |
author_facet | Lei, Lei Wang, Ying Xue, Qiong Tong, Jianhua Zhou, Cheng-Mao Yang, Jian-Jun |
author_sort | Lei, Lei |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7047869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70478692020-03-05 A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection Lei, Lei Wang, Ying Xue, Qiong Tong, Jianhua Zhou, Cheng-Mao Yang, Jian-Jun PeerJ Bioinformatics 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. PeerJ Inc. 2020-02-25 /pmc/articles/PMC7047869/ /pubmed/32140301 http://dx.doi.org/10.7717/peerj.8583 Text en ©2020 Lei et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Lei, Lei Wang, Ying Xue, Qiong Tong, Jianhua Zhou, Cheng-Mao Yang, Jian-Jun A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection |
title | A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection |
title_full | A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection |
title_fullStr | A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection |
title_full_unstemmed | A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection |
title_short | A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection |
title_sort | comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7047869/ https://www.ncbi.nlm.nih.gov/pubmed/32140301 http://dx.doi.org/10.7717/peerj.8583 |
work_keys_str_mv | AT leilei acomparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection AT wangying acomparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection AT xueqiong acomparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection AT tongjianhua acomparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection AT zhouchengmao acomparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection AT yangjianjun acomparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection AT leilei comparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection AT wangying comparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection AT xueqiong comparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection AT tongjianhua comparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection AT zhouchengmao comparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection AT yangjianjun comparativestudyofmachinelearningalgorithmsforpredictingacutekidneyinjuryafterlivercancerresection |