A Decisive Metaheuristic Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risk Assessment

Algorithm 1

Cluster K-means.

	Input:: Training set, : Number of clusters.
	Output:: Optimal attribute subset.
	Initial cluster center “” is selected;
	Compute distance from each attribute to every cluster “n” using Euclidean distance equations (2) and (3);
	Assign all attributes to nearest cluster based on cluster mean and similarity metric;
	Determine updated mean for every cluster;
	Repeat step 2 and 4;
	Terminate process on convergence condition;
	Drop irrelevant attributes which do not fit to any cluster;
	End.