Advancing Heart Disease Diagnosis: Deep Stacked Ensemble Architecture Utilizing ECG Signal Image and Advanced Feature Sets

Heart disease remains a leading cause of morbidity and mortality globally, necessitating continual advancements in diagnostic methodologies. Electrocardiography (ECG) serves as a fundamental tool for assessing cardiac health; however, traditional analysis methods often struggle with accurate classification of various cardiac abnormalities. This study aims to enhance heart disease diagnosis through the development of a novel diagnostic framework that integrates deep learning techniques, advanced feature extraction methods, and ensemble learning strategies. The proposed framework represents a significant departure from traditional approaches by leveraging a deep stacked ensemble architecture that combines ECG signal image processing with advanced feature sets. This integration enables the model to capture spatial and temporal patterns inherent in ECG signals, thereby improving diagnostic accuracy and reliability. The methodology involves transforming raw ECG signals into image representations and extracting both image-based features and advanced signal features. These features are then fed into a hierarchical ensemble architecture comprising multiple layers of deep learning models, followed by ensemble learning techniques to combine the outputs and enhance diagnostic performance. The proposed framework will be evaluated using a diverse dataset encompassing various cardiac conditions and healthy controls. Performance metrics such as accuracy, sensitivity, specificity, and AUC-ROC will be employed to assess the model’s efficacy. Comparative analyses against existing diagnostic approaches will also be conducted to demonstrate the superiority of the proposed methodology. Through the integration of deep learning, advanced feature extraction, and ensemble learning, the proposed framework has the potential to significantly advance heart disease diagnosis. By improving the accuracy and efficiency of diagnostic processes, this research could contribute to earlier detection, better patient outcomes, and reduced healthcare burdens associated with cardiovascular disease.