Cardiovascular Diseases (CVD) are one of the significant reasons for human mortality across the globe. Hence, more accurate and efficient models predicting the early stages of these diseases must be developed. In this research work, an attempt has been made to develop an appropriately huge and heterogeneous dataset after merging the three different datasets from IEEE, UCI and Kaggle sites. Several machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extra Trees (ET), Extreme Gradient Boost (XGB), gradient boosting, AdaBoost, and Multi-Layer Perceptron (MLP) have been employed on this integrated dataset. To improve the prediction accuracy even stacked models were employed in order to accomplish the objective of the research. The optimal combination of base models was gradient boosting, ET, and XGB with LR acting as the meta-model, yielding a high accuracy of 99.78% compared to the existing models. Such performances placed the meta-model far from the performance of the other models, which were found to be significantly erroneous in their outputs as compared to the former. This investigation demonstrates how datasets can be effectively merged to improve the generalization potential of a model and how ensemble and stacking methods could be used. The results present a comprehensive approach in building robust CVD prediction systems showing how sophisticated machine learning techniques can enhance Implementation overall decision.

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