@Article{iasc.2022.022160,
AUTHOR = {Zainab Arshad, Sohail Masood Bhatti, Huma Tauseef, Arfan Jaffar},
TITLE = {Heart Sound Analysis for Abnormality Detection},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {32},
YEAR = {2022},
NUMBER = {2},
PAGES = {1195--1205},
URL = {http://www.techscience.com/iasc/v32n2/45597},
ISSN = {2326-005X},
ABSTRACT = {According to the World Health Organization, 31% death rate in the World is because of cardiovascular diseases like heart arrhythmia and heart failure. Early diagnosis of heart problems may help in timely treatment of the patients and hence control death rate. Heart sounds are good signals of heart health if examined by an expert. Moreover, heart sounds can be analyzed with inexpensive and portable medical devices. Automatic heart sound classification can be very useful in diagnosing heart problems. Major focus of this research is to study the existing techniques for heart sound classification and develop a more sophisticated method. A signal processing technique is been proposed for heart sound classification. Five classifiers, Naive Bayes algorithm, Sequential (SMO), J48, Rep tree and Random Forest (RF) are used for this experiment. A detailed experimentation is performed to fine-tune the method and finally results are compared with the existing systems. The best proposed classifying technique results the overall accuracy of 91.33%.},
DOI = {10.32604/iasc.2022.022160}
}