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Fig. 3 | BMC Geriatrics

Fig. 3

From: Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population

Fig. 3

Interpretation of the model output. FRI, fall-related injury; CNS, central nervous system; ED, emergency department. A SHapley Additive exPlanations (SHAP) summary plot. The color represents the value of each feature, with red representing higher values and blue representing lower values. The SHAP value on the x-axis explains the direction and degree of the model’s prediction, where large positive values contribute to the prediction that a patient will experience fall-related injury, large negative values contribute to the prediction that a patient will not experience fall-related injury, and values close to zero contribute little to the prediction. B SHAP waterfall plot. Patient level prediction is depicted. Similarly, the SHAP value on the x-axis explains the direction and degree of the model’s prediction, where large positive values contribute to the prediction that a patient will experience fall-related injury, large negative values contribute to the prediction that a patient will not experience fall-related injury, and values close to zero contribute little to the prediction

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