Enhancing Heart Disease Prediction Through a Heterogeneous Ensemble DL Models

Accurate and highly effective forecasting approaches for timely detection and management are imperative given that cardiovascular illness is one of the biggest causes of death world wide. Machine learning (ML) and Deep learning (DL) methodologies have produced promising outcomes. In particular, ensemble DL models have gained attention for their ability in order to capitalize on the advantages of many models to enhance predictive performance. This study focuses on applying a cardiovascular disease prognosis with a DL ensemble. The model combines the predictions of multiple DL models, networks of neurons, such as supervised DL Models (CNN and RNN), to magnify accuracy and robustness. In this research, the effectiveness of an ensemble model is measured using an ample dataset, comparing it with individual DL models. The model’s prediction skills are evaluated by utilizing a verity of Evaluation metrics. The findings highlight the effectiveness of cardiovascular disease ensemble DL models prediction, showcasing their potential for enhancing diagnostic accuracy in clinical settings and aiding healthcare professionals in making informed decisions for patient care.