Advanced Healthcare for Heart Disease Prediction using Deep Learning Algorithms: A Comprehensive Analysis

Heart disease remains a leading cause of mortality worldwide, significantly contributing to the global burden of disease. Among various types, coronary heart disease accounts for the highest number of deaths. Both machine learning (ML) and deep learning (DL) models are instrumental in detecting heart diseases; however, DL models, particularly those with deeper architectures, excel in extracting complex features, making them highly effective in analysing electrocardiogram (ECG) signals for diagnosis. ECG signals provide critical insights into heart functionality, enabling early detection of abnormalities and conditions associated with heart disease. This study aims to develop an advanced deep learning model integrated with cloud technology to ensure accessibility and scalability. By leveraging the cloud, the model can facilitate real-time processing and remote diagnosis, making it a robust tool for healthcare providers. Additionally, the integration enhances the model's usability for large-scale deployment, ensuring timely detection of heart disease. This approach not only supports early intervention but also holds potential for reducing the mortality rate associated with heart-related conditions.