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Fig. 8 | BMC Medical Informatics and Decision Making

Fig. 8

From: A novel method for screening malignant hematological diseases by constructing an optimal machine learning model based on blood cell parameters

Fig. 8

Results of Shapley additive explanation (SHAP) analysis of the ANN model. SHAP summary plot of 20 feature clusters, derived by aggregating related values of a particular feature (e.g., the average, minimum, and maximum). Each dot corresponds to the SHAP value of the feature cluster for the malignant haematological diseases risk score of a given case patient or control subject at a certain point in time. A feature’s SHAP value (x-axis) represents the contribution of the specific feature to the risk score, with positive values indicating a contribution that increases the risk score and negative values indicating a contribution that lowers the score. The location of the dot on the x-axis represents its SHAP value, whereas its color represents the cluster’s value (the actual value of the feature that is represented in the cluster), with red representing higher values (for features measured along a continuum) or affirmative responses (for binary features). The dots are piled up vertically to show their density. The feature clusters are sorted by their mean absolute SHAP values

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