ENHANCED CARDIOVASCULAR DISEASE DIAGNOSIS USING MEL-FREQUENCY CEPSTRAL COEFFICIENTS (MFCC) AND MACHINE LEARNING: A COMPARATIVE ANALYSIS OF MACHINE LEARNING CLASSIFIERS

Owing to the fact that cardiovascular diseases (CVDs) are one of the main causes of mortality at the global level, so these diseases must be addressed. This study has approached the reported problem through the signals processing of the heart sounds. In particular, state of the art feature Mel-frequency Cepstral Coefficients (MFCC) has been extracted from the cardiac sound waves. Apart from that, five machine learning classifiers—Bernoulli Na¨ıve Bayes (BernoulliNB), Gaussian Na¨ıve Bayes (GaussianNB), Support Vector Machine (SVM), Random Forest, and k- Nearest Neighbors (kNN)—have been used to extract MFCC features in order to categorize heart sound data. In order to check the robustness of these classifiers, frequently used validation metrics like F1 Score, Accuracy, Precision, Recall, G-mean, and Specificity have been employed. The ensuing results demonstrate that the SVM classifier outperforms all the other classifiers showing the highest accuracy and resilience in the identification of cardiovascular disease. By providing important insights into the unique properties of cardiac sound signals linked to various cardiovascular illnesses, the use of MFCC features improves diagnostic capacities. Apart from that, the proposed non-invasive diagnostic method for cardiovascular diseases yields a possible path towards the early identification and treatment. The findings demonstrate how MFCC data may be utilized to efficiently and precisely identify cardiovascular illnesses using machine learning methods, particularly SVM.