Research Article
A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques
| Algorithm |
| 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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