Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors

Figure 3 The Shapley additive explanation value analysis diagram of the XGBoost model is used to explain the importance and direction of the features that affect the overall survival and progression-free survival. A: In the XGBoost model with overall survival as the endpoint, the average contribution of each feature to overall survival is shown; B: In the XGBoost model with overall survival as the endpoint, with the change of the size of the value of each feature, the distribution of their detailed Shapley additive explanation values; C: In the XGBoost model with progression-free survival as the endpoint, the average contribution of each feature to progression-free survival is shown; D: In the XGBoost model with progression-free survival as the endpoint, with the change of the size of the value of each feature, the distribution of their detailed Shapley additive explanation values. PLPR: Platelet count/(lymphocyte count × serum prealbumin); BMI: Body mass index; PNI: Serum albumin + 5 × lymphocyte count; RBC: Red blood cell; SHAP: Shapley additive explanation.