Blended Ensemble Learning Prediction Model for Strengthening Diagnosis and Treatment of Chronic Diabetes Disease

Algorithm 1

Stages in the proposed ensemble learning (EL) model.

(i)	Input required: Diabetes Dataset
(ii)	Output expected: Prediction of Ensemble Technique
	Step_1: Preprocessing on the Pima dataset of diabetes
	Step_2: Separate the dataset as test and training data
	Step_3: Construct EL (Majority voting) using BN and RBF with k-Fold-cross-Validation (k = 10)
	Step_4: Let us call each new sample “S” Test data
	Step_5: EL (test sample) = majority voting (class (RBF), class (BN))
	Step_6: Keep track of the suggested approach’s accuracy and evaluate it using a variety of measures.