Heart Valve Disease Classification using Artificial Intelligence Mechanism

Considering the exceedingly elevated mortality rate and the rising worldwide prevalence of cardiovascular diseases (CVDs), it is imperative to develop early diagnostic techniques. The industry has traditionally depended on phonocardiogram (PCG) indications due to their affordability and ease of use. PhysioNet is an innovative and successful technology that utilizes Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) to automatically classify cardiac auscultation types, including protrusion of the mitral valve, mitral valve regurgitation, aortic stenosis, and mitral stenosis. The procedure was automated through two learning steps: sequence residual acquisition and learning representations. The main objective is to analyze PCG data to classify illnesses. This study aims to develop a forecasting model that can classify the four distinct types of cardiovascular diseases according to their sounds using the Deep Learning methodology. Using the given variable, identify the sickness category. To determine the anticipated classes in measured performance metrics.