Abstract:
Based on the data from Basic Health Research (Riskesdas), the incidence of heart and blood vessel disease is increasing from year to year. At least 15 out of 1000 people ...Show MoreMetadata
Abstract:
Based on the data from Basic Health Research (Riskesdas), the incidence of heart and blood vessel disease is increasing from year to year. At least 15 out of 1000 people in Indonesia suffer from heart disease. The lack of early detection of heart disease makes sufferers of this disease increase. Also, general practitioners as the first health facility visited by patients do not have the ability like a cardiologist does in examining the heart. Therefore, an application of an android-based heart rhythm abnormality classification is made for general practitioners in an effort to overcome this problem as early detection of heart abnormalities. This application utilizes a portable ECG recording device (Electrocardiogram) to record the patient's ECG signal. The recorded ECG signal is then extracted by taking the values of PT interval, Bpm, RR interval, and local RR to be classified using machine learning with the Naïve bayes algorithm. The accuracy obtained by using naive bayes is about 75%. The results of this application can assist general practitioners in early detection of heart abnormalities and as a reference in the development of research on early detection of ECG signal abnormalities.
Date of Conference: 16-16 November 2021
Date Added to IEEE Xplore: 13 June 2022
ISBN Information: