Fig. 3: Comparison of features predictive of patient mortality across individual dementia type-specific models.
From: Machine learning models identify predictive features of patient mortality across dementia types
A Clustered heatmap of the top features across dementia types. Only features with a normalized SHapley Additive exPlanations (SHAP) value greater than 2.5 (explaining at least 2.5% of the prediction) in any given dementia type-specific model were included. The maximum normalized SHAP value for the clustered heatmap was set to 10 so that color contrasts were more discernable. The “No Dementia” category corresponds to patients receiving a primary etiologic diagnosis of “Not applicable, not cognitively impaired.” The “Unknown” category corresponds to patients receiving a primary etiologic diagnosis of “Missing/unknown.” “The “Other” category corresponds to patients receiving a primary etiologic diagnosis of “Cognitive impairment for other specified reasons (i.e., written-in values).” B SHAP beeswarm plots of six of the eight dementia type-specific models, excluding “Unknown” and “Other”. Features in the beeswarm plots are ranked by mean absolute SHAP value. The SHAP value distribution of the top ten most important features in each dementia type-specific model is displayed.
