Machine learning-based classification of cardiac diseases from PCG recorded heart sounds

Cardiovascular diseases are one of the most fatal diseases across the globe. Clinically, conventional stethoscope is used to check the medical condition of a human heart. Only a trained medical professional can understand and interpret the heart auscultations clinically. This paper presents a machine learning-based automatic classification system based on heart sounds to diagnose cardiac disorders. The proposed framework involves strategic processing and framing of heart sound to extract discriminatory features for machine learning. The most prominent features are selected and used to train a supervised classifier for automatic detection of cardiac diseases. The biological abnormalities disturbing the physical functioning of the heart cause variations in the auscultations, which is strategically used in terms of some discriminatory features for machine learning-based automatic classification. The proposed method achieved 97.78% accuracy with the equal error rate of 2.22% for abnormal and normal heart sound classification. The experimental results exhibit that the performance of the proposed method in proper diagnosis of the cardiac diseases is high in terms of accuracy and has low error rate which makes the proposed algorithm suitable for real-time applications.