期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2015
卷号：9
期号：1
页码：143-156
DOI：10.14257/ijseia.2015.9.1.12
出版社：SERSC
摘要：Wearable sensor mobile technologies and machine learning techniques are considered as two of the key research areas in the computer science and healthcare application industries. Our main aim is to build a simple yet accurate mobile application that is capable of real-time diagnosis and monitoring of patients with Coronary Artery Disease (CAD) or heart disease which is a major cause of death worldwide. Most available mobile healthcare systems focus on the data acquisition and monitoring component with little attention paid to real-time diagnosis. In this work, we build an intelligent classifier that is capable of predicting a heart disease problem based on clinical data entered by the user or the doctor and by using machine learning algorithms. This diagnosis component is integrated in the mobile application with a real-time monitoring component that continuously monitors the patient and raises an alarm whenever an emergency occurs. Our results show that the proposed diagnosis component has proved successful with a classification performance accuracy of more than 85% with the cross-validation test. Moreover, the monitoring algorithm provided a 100% detection rate.
