A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques

Table 3

Proposed algorithm.

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Algorithm

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Procedure LOAD (heart_disease_data)

Procedure DATA_SPLIT (heart_disease_data)

 Train_data, Test_data = split (heart_disease_data,lables)

 return Train_data, Test_data

voting=”soft”

C1= Naive_Bayes (Training_data, Train_label, Testing_data)

C2= Random_Forest (Training_data, Train_label, Testing_data)

C3=Support_Vector_Machine (Training_data, Train_label, Testing_data)

C4= Gradient_Boosting (Training_data, Train_label, Testing_data)

Procedure ENSEMBLE_MODEL (Train_data, Train_label, Test_data)

soft_voting_classifier=concatenate (C1,C2,C3,C4)

soft_voting_classifier.fit (Train_data, Train_label)

predictions=soft_voting_classifier.predict(Testing_data)

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