Automated Rheumatic Heart Disease Detection from Phonocardiogram in Cardiology Ward

Rheumatic Heart Disease (RHD) is a preventable and treatable form of cardiovascular diseases. It is also re- ferred to as the ailment of the disadvantaged mainly affecting children and young adults. RHD is recognized as a global health priority by World Health Organization. This chronic heart condition silently deteriorates the normal function of the heart valves which can be detected as a heart murmur using a stethoscope. As the cardiac auscultation process is an elusive process, the clinician will always be tempted to refer the patient for expensive and sophisticated imaging procedures like echocardiography. In this study, a machine learning algorithm is developed to augment the limitation in the auscultation process and transform the stethoscope as a powerful screening tool. For this current study, an RHD heart sound data set is recorded from one hundred seventy subjects. A total of twenty-six features are extracted to model murmur due to RHD. Twenty-four classification and regression algorithms have been tested out of which the Cubic SVM has demonstrated su- periority with a classification accuracy of 97.1%, with 98% sensitivity, 95.3 % of specificity 97.6% precision. The corresponding positive predictive values (PPV) are 96% and 97% for normal and RHD respectively. The results are based on data collected from a cardiology ward where there are more pathological cases than con- trols. Hence it is a valuable detection tool in a cardiology clinic. But in the future, integrating this machine learning algorithm with a mobile phone can be a powerful screening tool in places where access to echocardiography and cardiologist is difficult. Thus, it can then aid a timely, affordable and reliable detection tool allowing a non-medically trained individual to screen and detect RHD.

ISBN:978-989-758-398-8

Jaar van publicatie:2020

BOF-keylabel:ja

IOF-keylabel:ja

Authors from:Higher Education

Toegankelijkheid:Open