• T.Jayasudha
• Dr.R.Uma Rani

Cardiovascular disease (CVD) is a common health issue in the world as well, and it is noteworthy to detect it at the early stages and save lives. Single machine learning models are not concerned with the complex data of cardiovascular diseases. Ensemble methods are usually more successful as they are a mixture of numerous models to attain more results. However, existing methods are either interested in accuracy or they mix models in naive ways. This research is inspired by the need to possess a superior system that would integrate a strong ensemble model together with an intelligent voting technique. This paper introduces a new Weighted Performance Voting Ensemble-based Cardiovascular Disease Detection System (WPVE-CVDDS) that combines various sophisticated ensemble models, such as Bagging-Boosting Stacking, Soft Voting Ensemble, Bootstrap Ensemble, Augmented Stacking, and Sequential Boosting. They suggest a special weighted performance voting ensemble approach, based on the accuracy rates and false negative rates of models, to improve the reliability of prediction. The pre-processing pipeline uses the missing data imputation method of MissForest, the outlier detection method of the Local Outlier Factor, the class balancing method of K-means with SMOTE, the feature selection method of the mutual information, and the categorical data transformation method of target encoding with cross-validation. The experimental outcomes on real-world cardiovascular data show that the proposed hybrid system is superior and more robust than the traditional techniques and has the potential to be used to effectively screen cardiovascular risks and support decision-making.

Cardiovascular Disease, Ensemble classification, Pre-processing, weighted performance voting ensemble approach, Predictive System

"A Weighted Performance Voting Ensemble-Based Cardiovascular Disease Detection System Using Accuracy and False Negative Rate Optimisation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.f366-f376, September-2025, Available :http://www.jetir.org/papers/JETIR2509547.pdf

"A Weighted Performance Voting Ensemble-Based Cardiovascular Disease Detection System Using Accuracy and False Negative Rate Optimisation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppf366-f376, September-2025, Available at : http://www.jetir.org/papers/JETIR2509547.pdf