Phonocardiogram Analysis for Cardiovascular Disease Screening Using K-Star Algorithm

Cardiovascular disease stands out as one of the most prevalent health issues among the population. The accurate diagnosis and effective treatment of this disease are of paramount importance. The primary objective of this research is to propose a novel model for the automatic classification of heart sounds, specifically targeting the analysis of phonocardiograms to aid in the screening and diagnosis of cardiovascular disease. In this study, a dataset consisting of ۹۴۲ samples, recorded heart sounds, each characterized by ۲۳ features. The K-Star algorithm was employed for the classification of heart sounds. The K-Star algorithm is a model-based learning method that utilizes entropy theory as a distance measure. This approach maximizes the extraction of information from available data, offering a consistent methodology for managing both symbolic features and missing values effectively. The algorithm calculates the distance between two samples by considering the complexity of transforming one sample into another. The Waka tool was employed to implement this algorithm. Through the utilization of the K-Star algorithm, the accuracy of phonocardiogram analysis for cardiovascular disease screening was significantly enhanced, achieving a notable accuracy rate of ۸۰.۸۹۱۷%. This research contributes to the development of a reliable and efficient tool for the automatic classification of heart sounds, aiding in the early detection and screening of cardiovascular diseases.

کلیدواژه ها:

Phonocardiogram Analysis ، Screening ، Cardiovascular disease ، K-Star Algorithm