Table 5.

Responsiveness and reliability in predicting mortality for the 10 models developed using all 15 variables

TKA Mortality
	Reliability (Accuracy)			Responsiveness (AUC)
	Training	Testing	Validation	Training	Testing	Validation
Random Forest	93.49%	93.49%	93.47%	0.941	0.687	0.749
Neural Network	93.47%	93.49%	93.47%	0.816	0.938	0.996
XGT Boost Tree	99.97%	99.97%	99.98%	0.921	0.839	0.954
XGT Boost linear	99.97%	99.97%	99.81%	0.982	0.938	0.997
LSVM	99.87%	99.89%	99.89%	0.981	0.944	0.997
CHAID	99.87%	99.89%	99.89%	0.978	0.901	0.991
Decision List	99.90%	99.90%	99.91%	0.845	0.925	0.871
Discriminant	86.61%	86.72%	86.39%	0.894	0.97	0.93
Logistic Regression	93.21%	93.26%	93.17%	0.86	0.932	0.996
Bayesian Network	93.47%	93.49%	93.47%	0.931	0.821	0.632