A Survey on Prediction of Heart Functionality Sound by using Deep Learning Algorithm

Abstract

The primary objective of this study is to enhance the precision of predicting cardiovascular illnesses by introducing a sophisticated system that leverages deep learning techniques. Traditional diagnostic methods have relied on the analysis of cardiac sounds, achieving an accuracy of approximately 87.5% through the use of machine learning algorithms such as Random Forest and Decision Trees. However, these conventional approaches have their limitations. In contrast, the proposed hybrid approach aims to surpass these limitations by focusing on capturing intricate patterns and temporal relationships within heart sound data using deep learning. This innovative approach seeks to demonstrate a substantial improvement in prediction accuracy when compared to existing methods. If proven effective, this deep learning-based diagnostic tool has the potential to provide a more nuanced understanding of heart function, enabling early identification of anomalies and ultimately leading to improved patient outcomes in cardiovascular health.