Fig. 3: SHAP values of individual features of ML models. | British Journal of Cancer

Fig. 3: SHAP values of individual features of ML models.

From: Machine learning-based decision support model for selecting intra-arterial therapies for unresectable hepatocellular carcinoma: A national real-world evidence-based study

Fig. 3

a Bar plot of the average SHAP values for the top predicted features to illustrate global feature importance in class 1 (high risk) and class 0 (low risk) for the LGBM model in the IAT combination therapy group. b SHAP values of the top features of the CatBoost model in the IAT alone therapy group. The plot sorts feature by the sum of SHAP value magnitudes over all samples. The colour represents the feature value (red high, blue low). c Bar plot of the average SHAP values for the top predicted features to illustrate global feature importance for the LGBM model in the TACE therapy group. d SHAP values of the top features of the CatBoost model in the HAIC group. SHAP Shapley Additive exPlanations, XGBoost eXtreme Gradient Boosting, CatBoost Categorical Gradient Boosting, GBDT Gradient Boosting Decision Tree, LGBM Light Gradient Boosting Machine and RF Random Forest, HCC hepatocellular carcinoma, HAIC hepatic arterial infusion chemotherapy, TACE transarterial chemoembolization, IAT intra-arterial therapy.

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