Classification of cardiac disorders based on electrocardiogram data using a decision tree classification approach with the C45 algorithm - Sumiati Sumiati; Viktor Vekky Ronald Repi; Penny Hendriyati; Anharudin Anharudin; Afrasim Yusta; Agung Triayudi (IAES International Journal of Artificial Intelligence (IJ-AI), 2023)
The limitations of medical personnel, especially heart disease, cause difficulties in diagnosing heart disorders, so diagnosing heart disorders is not easy, it takes the ability and experience of a cardiologist who has the expertise and experience to be able to accurately diagnose heart disorders. Several studies in the field of computing have been carried out in diagnosing cardiac abnormalities in patients. This study was conducted to accurately test the results of the classification of heart disorders using electrocardiogram medical record data with a C.45 decision tree approach. The results showed that the classification of heart defects obtained a mean squared error (MSE) value of 0.24, a root mean squared error (RMSE) value of 0.49, and an accuracy value of 75.33% with the C4.5 algorithm.