Digital Stethoscope for Diagnosing the Heart Disease Using Machine Learning

Cardiovascular diseases are the foremost contributor to mortality in the world, and therefore continuous monitoring and early detection are essential. A new digital stethoscope system with heart rate sensor and near-infrared (NIR) sensor that can be utilized for heart rate collection and non-invasive glucose monitoring. Capturing heart sound signals and NIR light intensity, pre-processing the signals using digital signal processing methods, and then passing the data to the machine learning models that incorporate Bagging as the methodology to predictive cardiac health analysis, the system uses GSM modules for real-time alerts for any anomaly sensed with a web-based dashboard for real-time analytics and data logging over long periods. This method offers proactive and remote cardiac health monitoring through artificial intelligence based on IoT-based connectivity. The system proposed enhances accessibility, assists in reducing the reliance on conventional clinical environments, and promotes early intervention for cardiovascular diseases.