IMPLEMENTATION OF THE DECISION TREE C4.5 ALGORITHM IN THE DESIGN OF HEART DISEASE MEDICAL DATA INFORMATION SYSTEMS

Abstract

The heart is a muscle that is divided into four chambers, namely the right and left atria (atria) at the top, while two more chambers are located at the bottom, namely the right and left ventricles. In practice, in the world of medical education, medical record data for heart disease is often stored for the purpose of learning or processing it into knowledge. On the website https://www.kaggle.com/, there are many datasets that are stored and processed for learning purposes, including disease datasets, population datasets or other types of datasets. However, in reality, processing medical record data into knowledge is not easy, because the large number of recorded medical record data makes it impossible for humans to process it. In addition, conventional medical record data management has a poor level of accuracy so that the conclusions drawn will certainly be different and not so accurate. Because of these problems, it is necessary to apply data mining techniques in the medical field, especially medical record data management. In this study, the data mining algorithm used is Decision Tree C4.5. This algorithm was chosen because it is quite accurate and complex in the process of managing medical record data. The results showed that the application of the Decision Tree C4.5 algorithm was quite accurate in predicting heart disease medical record data where the test results obtained an accuracy of 96%.