Prediction of graft outcomes after kidney transplantation: When standard statistics compare to machine learning techniques

Table 9 Most important features according to permutation feature importance and Shapley additive explanations methods

Model and data	Relevant predictors and the data set from which they come	Mean of error increase (from PFI)	Standard deviation of error increase (from PFI)	Mean and direction of SHAP values
GB with D	Creatinine (D)	0.05	0.02	0.07 (+ -)
	Age (D)	0.03	0.01	0.09 (+)
	Stroke death (D)	0.02	0.01	0.03 (+)
	ECD (D)	0.02	0.02	0.03 (+)
	MBP (D)	0.01	0.01	0.01 (-)
RF with DT	Age (D)	0.06	0.02	0.03 (+)
	MBP (D)	0.04	0.01	0.02 (-)
	Cold ischemia time (T)	0.03	0.04	0.05 (+)
	Creatinine (D)	0.01	0.01	0.02 (+ -)
	Warm ischemia time (T)	0.01	0.02	0.05 (+)
GB with DR	Age (D)	0.04	0.01	0.03 (+)
	Smoking (R)	0.03	0.02	0.02 (+)
	MBP (D)	0.02	0.01	0.01 (-)
	Creatinine (D)	0.02	0.01	0.01 (+ -)

SHAP: Shapley values; ECD: Extended criteria donor; MBP: Mean blood pressure; D: Donor data; DT: Donor and transplant data; DR: Donor and recipient data; RF: Random forest; GB: Gradient boost; PFI: Permutation feature importance.