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

Algorithm 8

Naive Bayes (NB).

	Invalue: data (n-dimensional), X1 ϵ R1n1 and outvalue (target), Y1 ϵ R1
	Outvalue: The pp, P1 ϵ [0, 1] of test data (unseen), x, where
	, C1 = 2 (diabetes present (C1) or not (C2))
(1)	Assign the probabilities (prior) for each class,
	and , where N determines the number of samples
(2)	The output pp of class for the given predictor (attributes)
	P1(X1|Ci1) is the predictor (likelihood) for a given class and P(X1) is the pp (prior).