Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without non-alcoholic fatty liver

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 12, Issue 15

This Article

Academic Content and Language Evaluation of This Article

CrossCheck and Google Search 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

All Articles published online

Item

Count

PDF

HTML

1213

Figures (1-4)

173

Tables (1-6)

140

Sum=1568

Publishing Process of This Article

Item

Count

Browse

Download

270

Sum=325

May 26, 2024 (publication date) through Feb 2, 2025

Times Cited of This Article

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. May 26, 2024; 12(15): 2506-2521
Published online May 26, 2024. doi: 10.12998/wjcc.v12.i15.2506

Full Size Figure Download Figure

Figure 2 Flowchart of the proposed machine learning methods. XGBoost: eXtreme gradient boosting.

Citation: Chen IC, Chou LJ, Huang SC, Chu TW, Lee SS. Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without non-alcoholic fatty liver. World J Clin Cases 2024; 12(15): 2506-2521
URL: https://www.wjgnet.com/2307-8960/full/v12/i15/2506.htm
DOI: https://dx.doi.org/10.12998/wjcc.v12.i15.2506