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

Algorithm 6

AdaBoost (AB).

	Invalue: data (n-dimensional), X1 ϵ R1^n1 with N samples and outvalue (target),
	Y1 ϵ R1
	Outvalue: The pp, P1 ϵ [0,1] of unseen test data, x1, where
	, C1 = 2 (diabetes in (C1) or not (C2))
(1)	Initiate the sample weight, D1(i1) =  , i1 = 1, 2,...,N.
(2)	for t1 ≤ T1 (n_Classifiers) do
(3)	Weak_learner_training by using distribution .
(4)	Select a hypothesis (weak), : R1^n1 ⟶ R1 with low weight error,
	_x000F_t1 =  [ () 6 = Y1 ]
(5)	Choose and update where i1 = 1,...,N and z1t1 is the normalization factor.
(6)	Output pp: .