Heart Sound Classification Using Signal Processing and Machine Learning Algorithm

Cardiovascular diseases” Heart attacks and strokes are often sudden, severe incidents. due to impediments that impede blood flow to the Often, heart attacks and strokes are sudden, severe events. resulting from impediments that impede blood flow to the heart or brain. Themajor cause of these obstructions is the deposition of fatty deposits on the inner walls of blood arteries. Unlike traditionalmethods that rely on analysing heart sounds with a stethoscope, this hybrid approach aims to enhance prediction accuracy by capturing intricate patterns in heart function. RNN and LSTMexcel at processing sequential data, making them suitable for analysing The temporal aspect of heart sound patterns. By leveraging their ability to retain and process information over time, the system aims to provide a more nuanced understanding of heart function, thus improving prediction accuracy beyond the current 87.5% achieved via conventional machine learning techniques like decision trees and Random Forest. Through comparative analysis, this study seeks to demonstrate the potential for significantly enhanced prediction accuracy, offering a more robust diagnostic tool for early detection of cardiovascular diseases, thereby Improving patient outcomes and alleviating the burden of heart-related health issues.