Computational Intelligence and Neuroscience

Research Article

AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease

Pseudocode of NB classifier.

	Input required: TDS: Training Dataset TDS = Output expected: Class Labels YES and NO
	Step_1: The Given Dataset TDS, consists of symptoms pertaining to different classes, say YES and NO
Step_2: Calculate prior-probability of “YES” class = No of attributes of class YES/Total no of attributes Compute prior-probability of “NO” class = No of attributes of class NO/Total no of attributes
	Step_3: Compute , total no. of attributes that are frequent for each class
	= Sum of frequent attributes of class YES
	= Sum of frequent attributes of class NO
	Step_4: Compute the conditional probability
	= attributeCount/
	= attributeCount/
	= attributeCount/
	= attributeCount/
	…
	= attributeCount/
	Step_5: Classify a new record of attributes of a patient based on the probability P (NEW/feature).
	Compute = ∗
	Compute = ∗
	Step_6: Assign the new record of patient to either class YES or NO which has higher probability.