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