Enhanced Heart Functionality Prediction Using Acoustic Signals and Deep Learning Algorithms

Cardiovascular diseases primarily present as acute events as the result of block or blocks in the arteries created by a build up in fatty material that slows or cuts off blood supply to the heart or brain [10]. The classic approach to diagnosing heart disease and predicting heart attacks or strokes is by analysis of heart sounds via auscultation with a stethoscope. The proposed hybrid approach utilizes superior methods, namely, Convolutional Neural Networks and Gated Recurrent Units for advanced analysis in heart sound. CNNs particularly perform well by extracting spatial pattern from data, and GRU handles temporal dependency efficiently, therefore this combination could be more favorable for sequential analysis of heart sound. With the usage of both models, it will predict cardiovascular events by patterns and anomalies in heart sounds that most traditional algorithms fail to identify, such as Random Forest and Decision Trees. Utility of the possibility of working with heart sound data for detection of changes; those changes that are associated with the effects caused by conditions like pericardial effusion due to cancer metastasis on cardiac structure. This system, therefore, will prove comparatively how the system might predict much more accurately than others, hence making it a stronger diagnostic tool for early detection of cardiovascular diseases that will benefit more patients and cut down the heart-related health issues..