Efficient Model for Coronary Artery Disease Diagnosis: A Comparative Study of Several Machine Learning Algorithms

Table 5

The specifications and settings of algorithms including parameters.

Algorithm	Setting
MLP	Batch size = 100, learning rate = 0.3, momentum = 0.2, number of decimal places = 2
		SVM	Batch size = 100, the polynomial kernel: K (x, y) = <x, y>^p or K (x, y) = (<x, y >+ 1) ^p, random seed = 1, tolerance parameter = 0.001 number of decimal places = 2
LR	Batch size = 100, max lets = −1, ridge = 1.0E−8, number of decimal places = 4
		J48	Batch size = 100, confidence factor = 0.25, min num obj = 2, num folds = 3, seed = 1, number of decimal places = 2
RF	Batch size = 100, number of iterations = 100, seed = 1, number of decimal places = 2
		KNN	Batch size = 100, number of decimal places = 2
NB	Batch size = 100, number of decimal places = 2