Copyright
©The Author(s) 2024.
World J Gastroenterol. Oct 28, 2024; 30(40): 4354-4366
Published online Oct 28, 2024. doi: 10.3748/wjg.v30.i40.4354
Published online Oct 28, 2024. doi: 10.3748/wjg.v30.i40.4354
Figure 2 Kaplan-Meier survival curve analysis of risk score based on XGBoost.
A: When XGBoost was used to predict the patient’s overall survival, the Kaplan-Meier curve of the risk score in the training set; B: When XGBoost was used to predict the patient’s overall survival, the Kaplan-Meier curve of the risk score in the test set; C: When XGBoost was used to predict the patient’s progression-free survival, the Kaplan-Meier curve of the risk score in the training set; D: When XGBoost was used to predict the patient’s progression-free survival, the Kaplan-Meier curve of the risk score in the test set.
- Citation: Li HW, Zhu ZY, Sun YF, Yuan CY, Wang MH, Wang N, Xue YW. Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors. World J Gastroenterol 2024; 30(40): 4354-4366
- URL: https://www.wjgnet.com/1007-9327/full/v30/i40/4354.htm
- DOI: https://dx.doi.org/10.3748/wjg.v30.i40.4354