Effective Ensemble Deep Learning Approach to Coronary Heart Disease Prediction

Cardiovascular disease is an important global challenge, and timely identification along with effective treatment relies on the accurate diagnosis of the condition. This work seeks to increase the precision of cardiovascular disease prediction by utilizing ensemble deep learning approaches. Many algorithms for deep learning, such as C-NNs), R-NNs, and G-BMs, are used to build a collective model. The ensemble model surpasses individual models in prediction accuracy by capitalizing on the strengths of each model. To improve the predictive capabilities of the models, the researchers also examine techniques in feature engineering, such as feature extraction and selection. Due to the widespread nature of heart disease as a global health concern, accurately forecasting its incidence is essential for effective initial assessment and timely intervention. This study explores the use of ensemble deep-learning techniques to improve the precision of heart disease predictions. The main goal of the research is to create an ensemble model that combines various deep learning methods, such as C-NNs, R-NNs, and G-BM.The purpose of adopting an ensemble strategy is to leverage the collective strengths of individual models and their distinct benefits to achieve enhanced predictive performance compared to separate models.The researchers also consider feature extraction and selection as methodologies to further boost the predictive power of the ensemble model.