Abstract
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, claiming over 17 million lives annually. Early detection of conditions like heart murmurs, often indicative of heart valve abnormalities, is critical for improving patient outcomes. Traditional diagnostic methods, including physical auscultation and advanced imaging techniques, are constrained by their reliance on specialized clinical expertise, inherent procedural invasiveness, substantial financial costs, and limited accessibility, particularly in resource-limited healthcare environments. This study presents a novel convolutional recurrent neural network (CRNN) model designed for the non-invasive classification of heart murmurs. The model processes heart sound recordings using advanced pre-processing techniques such as z-score normalization, band-pass filtering, and data augmentation (Gaussian noise, time shift, and pitch shift) to enhance robustness. By combining convolutional and recurrent layers, the CRNN captures spatial and temporal features in audio data, achieving an accuracy of 90.5%, precision of 89%, and recall of 87%. These results underscore the potential of machine-learning technologies to revolutionize cardiac diagnostics by offering scalable, accessible solutions for the early detection of cardiovascular conditions. This approach paves the way for broader applications of AI in healthcare, particularly in underserved regions where traditional resources are scarce.