Efficient Predictive Modelling of Cardiovascular Disease using Deep Learning Approaches

In order to improve the identification and diagnosis of cardiovascular diseases (CVDs), this study provides a fresh suggested model that uses Graph Convolutional Networks (GCNs) for the multi-label classification of phonocardiographic data. The complex temporal and spectral properties included in heartbeat sounds are often too complex for traditional machine learning frameworks to fully capture. Mel-Frequency Cepstral Coefficients (MFCC) and MEL-spectrograms, two sophisticated feature extraction techniques, work together in our suggested model to improve the representation of important audio aspects that are essential for cardiovascular diagnoses. Through the utilization of GCNs' intrinsic relational structure, the model enhances the propagation of superior information among associated nodes, which in turn improves the categorization of various cardiovascular conditions. Moreover, dynamic customization of node feature representations is made possible by the design, which enhances the resilience of real-world pulse recordings against noise and fluctuation. The results highlight the usefulness of the suggested model in clinical settings, opening the door for further research into hybrid modelling methods and the incorporation of advanced brain architectures with the goal of improving cardiovascular health assessment and intervention plans.