Cardiovascular diseases remain a leading cause of death worldwide, responsible for nearly 47% of deaths in developed countries, highlighting the urgent need for better diagnostic tools.

Advanced data processing techniques, including deep learning algorithms and entropy-driven methods, are employed to optimize feature extraction and improve model performance.

Early diagnosis is crucial in managing cardiovascular diseases, which affect millions and can lead to severe health complications if not detected promptly.

Convolutional neural networks are used for automated image analysis, enabling faster and more accurate diagnostics in cardiac imaging.

The hybrid approach achieves up to 96% diagnostic accuracy, significantly surpassing traditional models that rely solely on clinical data, demonstrating the power of integrating multiple data sources.

This innovative research advances the application of artificial intelligence in healthcare, especially in cardiology, contributing valuable insights to the field.

The study underscores the importance of developing accessible and cost-effective diagnostic methods to replace invasive procedures, reducing risks and healthcare costs.

This multimodal framework not only improves diagnostic accuracy but also promotes accessible, cost-effective alternatives to invasive procedures such as angiography, which carry risks and high costs.

Researchers are integrating machine learning with medical imaging technologies like cardiac MRI and echocardiography to improve the detection and diagnosis of heart diseases, aiming for more accurate and non-invasive methods.

A hybrid machine learning model has been developed that combines imaging data with clinical information, enhancing decision-making and enabling early detection of cardiovascular conditions.