Cardiovascular Sound Classification Using Neural Architectures and Deep Learning for Advancing Cardiac Wellness

Cardiovascular diseases (CVDs) remain one of the foremost causes of death globally; hence, the need for several must-have, advanced automated diagnostic solutions towards early detection and intervention. Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability. To counter this limitation, this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat. We employ FastAI vision-learner-based convolutional neural networks (CNNs) that include ResNet, DenseNet, VGG, ConvNeXt, SqueezeNet, and AlexNet to classify heart sound recordings. Instead of raw waveform analysis, the proposed approach transforms preprocessed cardiovascular audio signals into spectrograms, which are suited for capturing temporal and frequency-wise patterns. The models are trained on the PASCAL Cardiovascular Challenge dataset while taking into consideration the recording variations, noise levels, and acoustic distortions. To demonstrate generalization, external validation using Google’s Audio set Heartbeat Sound dataset was performed using a dataset rich in cardiovascular sounds. Comparative analysis revealed that DenseNet-201, ConvNext Large, and ResNet-152 could deliver superior performance to the other architectures, achieving an accuracy of 81.50%, a precision of 85.50%, and an F1-score of 84.50%. In the process, we performed statistical significance testing, such as the Wilcoxon signed-rank test, to validate performance improvements over traditional classification methods. Beyond the technical contributions, the research underscores clinical integration, outlining a pathway in which the proposed system can augment conventional electronic stethoscopes and telemedicine platforms in the AI-assisted diagnostic workflows. We also discuss in detail issues of computational efficiency, model interpretability, and ethical considerations, particularly concerning algorithmic bias stemming from imbalanced datasets and the need for real-time processing in clinical settings. The study describes a scalable, automated system combining deep learning, feature extraction using spectrograms, and external validation that can assist healthcare providers in the early and accurate detection of cardiovascular disease. AI-driven solutions can be viable in improving access, reducing delays in diagnosis, and ultimately even the continued global burden of heart disease.