Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD-Net and BlCVDD-Net

Abstract

Cardiovascular diseases (CVDs)” are one of the most common causes of death around the world, so it's important to get correct and quick diagnoses to lower the risks. This study uses advanced “Deep learning (DL) and machine learning (ML)” methods to make diagnoses more accurate. traditional ML methods rely largely on manual feature engineering, but DL methods are great at automatically extracting features, which makes them perfect for working with complicated datasets. This paper uses the heart ailment Dataset to deal with class imbalance through “Adaptive synthetic (Adasyn)” Oversampling and suggests a new ensemble-based detection approach. comprehensive tests show that the voting Classifier is better than any one model, “with an accuracy of 91.7%, a precision of 92.0%, a recall of 91.7%, and an F1-score of 91.8%”. these results show how well ensemble methods can work with different types of data and get excellent diagnostic accuracy. This shows how these methods could help improve CVD diagnosis.