Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation

Algorithm 2

Feature selection based on ICA.

	Invalue: n-dimensional data (original), X1 ϵ R1n1
	Outvalue: k-dimensional data (reduced), Y1 ϵ R1k1
(1)	The non-quadratic function is set and considered as nonlinear function and G1 is assumed as negentropy.
(2)	Given W1 of W1 × H1 = X1, where W1, H1, and X1, during mixing, are the source ratios
	Consisting of multiple components, where the output is mixed separately.
(3)	Obtain PCA on X1 by X1 = PCA(X1)
(4)	while W changes do
(5)	W1 = mean (X1 × G1(W1 · X1)) – mean (G0 (W1T1 · X1)),
(6)	W1 = orthogonalize (W1)
(7)	Execute Y1 = W1 · X1, where Y1 ϵ R1k1.