Machine learning approach to predict acute kidney injury after liver surgery

doi:10.12998/wjcc.v9.i36.11255

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 9, Issue 36

This Article

Academic Content and Language Evaluation of This Article

Academic Rules and Norms of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Number of Hits and Downloads for This Article

Total Article Views (4850)

All Articles published online

The chart showing PDF series, WORD series, HTML series, Figures (1-4) series, Tables (1-2) series.

Item

Count

PDF

328

WORD

158

HTML

2347

Figures (1-4)

250

Tables (1-2)

268

Sum=3351

Publishing Process of This Article

The chart showing Browse series, Download series.

Item

Count

Browse

379

Download

1120

Sum=1499

Dec 26, 2021 (publication date) through Dec 31, 2024

Times Cited of This Article

Times Cited (6)

Journal Information of This Article

Publication Name

World Journal of Clinical Cases

ISSN

2307-8960

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Study

World J Clin Cases. Dec 26, 2021; 9(36): 11255-11264
Published online Dec 26, 2021. doi: 10.12998/wjcc.v9.i36.11255

Full Size Figure Download Figure

Figure 3 Areas under the receiver operating characteristic curve. LR: Logistic regression; SVM: Support vector machine; RF: Random forest; XGboost: Extreme gradient boosting; DT: Decision tree; AUC: Area under the curve.

Citation: Dong JF, Xue Q, Chen T, Zhao YY, Fu H, Guo WY, Ji JS. Machine learning approach to predict acute kidney injury after liver surgery. World J Clin Cases 2021; 9(36): 11255-11264
URL: https://www.wjgnet.com/2307-8960/full/v9/i36/11255.htm
DOI: https://dx.doi.org/10.12998/wjcc.v9.i36.11255