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Fig. 4 | Journal of Translational Medicine

Fig. 4

From: An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation

Fig. 4

SHAP summary plot and dependence plot. A The SHAP summary plot demonstrated the general importance of each feature in GBM model. The color bar on the right indicates the relative value of a feature in each case. Red dots indicate high values and blue dots indicate low values. The violin graph lining up on the midline is the aggregation of dots representing each case in the internal validation set. The distance between the upper and lower margin of the violin graph represents the amount of the cases that end up with the same SHAP values offered by this feature. Categorical features including preoperative HE and HM and steatosis ≥ 1 were represented by 0 and 1, while “0” means “No” and “1” means “Yes”. B SHAP dependence plot demonstrated the distribution of SHAP output value of a single feature. In our GBM prediction model, higher IBIL, lower intraoperative urine output, longer time under anesthesia and lower preoperative PLT are correlated with higher SHAP values, representing higher probability of a prediction that favors the diagnosis of AKI

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