Table 4.

Results from PRBF algorithm from experts D1-D4. A) Cross-validation model training results for PRBF algorithm for Population size = 4000, stretch = 25, learning rate = 0.1, and training iterations = 100,000, B) True labels and predicted uncertain labels for the tested SFRP sample of fictitious patient number 72

A
	Training accuracy	Test accuracy
Fold-1	0.95	0.90
Fold-2	0.96	0.90
Fold-3	0.98	0.95
Fold-4	0.95	0.95
Fold-5	0.94	0.90
Mean accuracy	0.96	0.92
Standard deviation	± 0.01	± 0.03

B
Fictitious patient	Majority vote	Uncertain label ($\text{u}$)	PRBF prediction ($\pi$)
72	0	[1,0.5,0,0,0]	[0.979,0.321,0,0,0]