HeartEnsembleNet: An Innovative Hybrid Ensemble Learning Approach for Cardiovascular Risk Prediction

Healthcare, Journal Year: 2025, Volume and Issue: 13(5), P. 507 - 507

Published: Feb. 26, 2025

Cardiovascular disease (CVD) is a prominent determinant of mortality, accounting for 17 million lives lost across the globe each year. This underscores its severity as critical health issue. Extensive research has been undertaken to refine forecasting CVD in patients using various supervised, unsupervised, and deep learning approaches. study presents HeartEnsembleNet, novel hybrid ensemble model that integrates multiple machine (ML) classifiers risk assessment. The evaluated against six classical ML classifiers, including support vector (SVM), gradient boosting (GB), decision tree (DT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF). Additionally, we compare HeartEnsembleNet with Hybrid Random Forest Linear Models (HRFLM) techniques stacking voting. Employing dataset 70,000 cardiac 12 clinical attributes, our proposed achieves notable accuracy 92.95% precision 93.08%. These results highlight effectiveness enhancing prediction, offering promising framework support.

Language: Английский

Continual and wisdom learning for federated learning: A comprehensive framework for robustness and debiasing

Information Processing & Management, Journal Year: 2025, Volume and Issue: 62(5), P. 104157 - 104157

Published: April 9, 2025

Language: Английский