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

Algorithm 7

Random forest (RF).

	Invalue: data (n-dimensional), X1 ϵ R1n1, outvalue (target), Y1 ϵ R1
	Outvalue: The pp, P1 ϵ [0, 1] of test data (unseen), x1, where
	C1 = 2 (diabetes in (C1) or not (C2))
(1)	for b1 = 1 to N (n_Bagging) do
(2)	Design a sample (bootstrap) ( from given X1 ϵ , Y1 ϵ R1
(3)	Design an RF tree using and by recursively repeating.
(4)	The pp P1^NRF (x1) where is the prediction of the kth RF.