Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

doi:10.3748/wjg.v29.i43.5804

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 29, Issue 43

This Article

(169)

(179)

(0)

(15)

(5679)

(19)

Peer-Review Report 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

Journal Information of This Article

Publication Name

World Journal of Gastroenterology

ISSN

1007-9327

Publisher of This Article

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

Retrospective Study

World J Gastroenterol. Nov 21, 2023; 29(43): 5804-5817
Published online Nov 21, 2023. doi: 10.3748/wjg.v29.i43.5804

Full Size Figure Download Figure

Figure 8 SHAP analysis of the XGBoost model. A: Visual representation of each feature in the XGBoost model and the relationship between the importance of each feature. The color represents the value of the variable, with red representing the larger value and blue representing the smaller value. AFP: Alpha-fetoprotein; GLR: γ-glutamyl transferase to lymphocyte ratio.

Citation: Zhang YB, Yang G, Bu Y, Lei P, Zhang W, Zhang DY. Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma. World J Gastroenterol 2023; 29(43): 5804-5817
URL: https://www.wjgnet.com/1007-9327/full/v29/i43/5804.htm
DOI: https://dx.doi.org/10.3748/wjg.v29.i43.5804

Zhang YB, Yang G, Bu Y, Lei P, Zhang W, Zhang DY. Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma. World J Gastroenterol 2023; 29(43): 5804-5817 [PMID: 38074914 DOI: 10.3748/wjg.v29.i43.5804]

All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.