Fig. 4
From: Machine learning-based models for the prediction of breast cancer recurrence risk
SHAP values and feature interaction scores in AdaBoost-based prediction. (a) The top 20 most important features for the prediction of BC recurrence (ranked from most to least important). (b) The distribution of the impacts of each feature on the model output. The colors represent the feature values: red for larger values and blue for smaller values. Abbreviations: CA125, carcinoma antigen 125; CEA: carcinoembryonic antigen; Fbg: fibrinogen; CA15-3, carcinoma antigen 15 − 3; FVIII, coagulation factor VIII; TPSA, tissue polypeptide-specific antigen; α2-AP, α2-antiplasmin; RBC, red blood cell; NEUT, neutrophils; PLR, platelet-to-lymphocyte ratio; WBC, white blood cell; PLT, platelet, SHAP, Shapley Additive Explanation