Machine learning algorithms can accurately classify heart sounds into normal and abnormal categories, potentially aiding in the automated diagnosis of structural or functional heart diseases.
According to global statistics and the world health organization (WHO), about 17.5 million people die each year from cardiovascular disease. In this paper, the heart sounds gathered by a stethoscope are analyzed to diagnose several diseases caused by heart failure. This research’s primary process is to identify and classify the data related to the heart sounds categorized in four general groups of S1 to S4. The sounds S1 and S2 are considered as the heart’s normal sounds, and the sounds S3 and S4 are the abnormal sounds of the heart (heart murmurs), each expressing a specific type of heart disease. In this regard, the desired features are first extracted after retrieving the data by signal processing algorithms. In the next step, feature selection algorithms are used to select the compelling features to reduce the problem’s dimensions and obtain the optimal answer faster. While the existing algorithms in the literature classify the sound into two groups of normal and abnormal, in the final section, some of the most popular classification algorithms are utilized to classify the type of sound into three classes of normal, S3 and S4 categories. The proposed methodology obtained an accuracy rate of 87.5% and 95% for multiclass data (3 classes) and 98% for binary classification (normal vs. abnormal) problems.
Zeinali et al. (2021) studied this question.