Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study

Table 3 Comparison of learning algorithms during cross-validation experiments

Algorithm	Dataset1	AUC (%)		P value2
		Mean	SD
Conventional statistics	Baseline clinical + biomarker data	65.9	9.5	-
Feed-forward ANN	Baseline clinical + biomarker data	71.0	8.7	0.25
Feed-forward ANN	Baseline biomarker data only	70.6	8.3	0.33
Recurrent neural network	Baseline and prior biomarker data	75.4	7.8	0.036

¹Clinical data refers to non-biochemical data such as age, sex, disease characteristics and concurrent treatments. Biomarker data refers to complete blood count, liver function tests and albumin.

²P value for comparison against conventional statistical algorithm, using the variance-corrected repeated k-fold t test. AUC: Area under the receiver operator characteristic curve; ANN: Artificial neural network.