Table 2.

Average performance for algorithms across trials with pre-operative information. The table presents means and standard errors separately for the four algorithms with the training set (fitting) and test set (prediction). For each combination, the table reports the mean and standard error of the mean for sensitivity, specificity and balanced accuracy across trials. FFTi, UDT and LogReg were tested in 10,000 trials and FFTd in the first 1000 of these only.

		Training			Prediction
		Sensitivity	Specificity	Bal. Accuracy	Sensitivity	Specificity	Bal. Accuracy
FFTi	M	0.693	0.682	0.688	0.578	0.644	0.611
	SE	(0.0013)	(0.0013)	(0.0002)	(0.0015)	(0.0014)	(0.0003)
FFTd	M	0.751	0.689	0.720	0.562	0.625	0.593
	SE	(0.0037)	(0.0037)	(0.0007)	(0.0044)	(0.0040)	(0.0011)
UDT	M	0.868	0.738	0.803	0.52	0.626	0.573
	SE	(0.0006)	(0.0006)	(0.0002)	(0.0010)	(0.0007)	(0.0004)
LogReg	M	0.737	0.747	0.742	0.581	0.692	0.637
	SE	(0.0004)	(0.0003)	(0.0003)	(0.0007)	(0.0004)	(0.0003)

M—mean, SE—standard error of the mean, FFTi—fast-and-frugal tree construction using the ifan algorithm, FFTd—fast-and-frugal tree construction using the dfan algorithm, UDT—unconstrained decision trees based on the CART algorithm, LogReg—logistic regression.