A Fact-Finding Procedure Integrating Machine Learning and AHP Technique to Predict Delayed Diagnosis of Bladder Patients with Hematuria

Table 2

Parameter setting in Weka.

Method	Parameters	Range	Value setting
C4.5	Confidence factor	0.1–0.5	0.25
	Minimum number of instances per leaf	2–20	20
Random forest	Number of trees	5–250	10
	Number of attributes to be used in random selection	2–8	4
Support vector machines	Kernel		PolyKernel
Multilayer perceptron	Number of hidden nodes	5–10	7
	Learning rate	0.1–0.6	0.3
	Momentum factor	0–0.9	0.2
	Maximum number of epochs	300–900	500