A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Algorithm 1

Pseudocode of the Relief algorithm.

	RELIEF Algorithm
	Require: for each training instance set S, a vector of feature values and the class value
	n ⟵ number of training instances
	a ⟵ number of features
	Parameter: m ⟵ number of random training instances out of n used to update W
	Initialize all feature weights W[A]: = 0.0
	For k: = 1 to m do
	Randomly select a “target” instance
	Find a nearest hit “H” and nearest miss (instances)
	For A: = 1 to a do
	W[A]: = W[A] − diff (A, , H)/m + diff (A, , M)/m
	End for
	End for
	Return the weight vector W of feature scores that compute the quality of features