Аннотация
This research paper presents the development of a Digital Stethoscopy System for the Detection of Heart Diseases using machine learning techniques on phonocardiograms. With cardiovascular diseases remaining a leading cause of global morbidity and mortality, the need for accurate and timely cardiac diagnostics is paramount. The proposed system integrates cutting-edge technology, including digital stethoscopy and machine learning, to address the limitations of traditional cardiac auscultation methods. The core components of the system, including phonocardiographic signal acquisition, an advanced Analog Front End, a robust microcontroller, and sophisticated machine learning algorithms, are meticulously designed and evaluated. Extensive data preparation, model training, and rigorous testing are undertaken to assess the system's diagnostic capabilities. Results indicate that this innovative system has the potential to provide reliable and real-time cardiac diagnostics, offering a promising solution for early detection and intervention in heart diseases. Furthermore, the research underscores the importance of large and diverse datasets for training machine learning models and emphasizes the significance of continuous validation and evaluation. The outcomes of this study hold promise for improving cardiac healthcare, enhancing patient outcomes, and advancing the field of cardiology, with recommendations for further research and clinical validation to refine and optimize the system for widespread clinical implementation.