Transformative Deep Learning Approaches For Accurate Detection Of Heart Abnormalities

Abstract

Heart abnormalities remain a leading cause of mortality worldwide, necessitating the development of precise diagnostic tools. This study explores the application of deep learning, a transformative approach in artificial intelligence, to enhance the detection of cardiac irregularities. Utilizing convolutional and recurrent neural networks, the proposed framework analyzes electrocardiogram (ECG) signals and medical imaging data to accurately identify arrhythmias, structural heart diseases, and other anomalies. Comprehensive experimentation on benchmark datasets demonstrates the model's robustness, scalability, and potential for integration into clinical practice. The findings underscore deep learning's promise as a non-invasive, efficient, and reliable solution for improving heart disease diagnosis and patient care