Early Identification of Potential Heart Abnormalities with Decision Tree Method

Heart abnormalities are serious health problems that require proper diagnosis and treatment. This study aims to identify potential heart abnormalities using the Decision Tree method with clinical data of user electrocardiograms used as input in model formation. In this study, 30 subjects using a portable ECG system were involved. The test results showed that the Decision Tree model was able to identify potential heart abnormalities with a high accuracy rate of 97%. This will help various parties in early detection of heart problems in each user, so that treatment or intervention can be done faster.