Early Heart Disease Detection Using Mel-Spectrograms and Deep Learning

Heart disease is a leading cause of morbidity and mortality worldwide, necessitating the development of innovative diagnostic methodologies for early detection. This study presents a novel deep convolutional neural network model that leverages Mel-spectrograms to accurately classify heart sounds. Our approach demonstrates significant advancements in heart disease detection, achieving high accuracy, specificity, and unweighted average recall scores (UAR), which are critical factors for practical clinical applications. The comparison of our proposed model's performance with a PANN-based model from a previous study highlights the strengths of our approach, particularly in terms of specificity and UAR. The successful application of Mel-spectrograms in conjunction with deep learning techniques illustrates the potential for widespread clinical adoption of our model, ultimately contributing to early detection and improved patient outcomes. Furthermore, we discuss potential avenues for future research to enhance the model's effectiveness, such as incorporating additional features and exploring alternative deep learning architectures. In conclusion, our deep convolutional neural network model, combined with Mel-spectrograms, offers a significant step forward in the field of heart sound classification and the early detection of heart diseases, demonstrating its potential for real-world clinical applications and improved patient outcomes.