Classification of Cardiac Disorders Based on Electrocardiogram Data with Fuzzy Cognitive Map (FCM) Algorithm Approach - Sumiati, .; Hoga, Saragih; T.K.A, Rahman; Viktor Vekky, Ronald Repi; Agung, Triayudi (2021)
In this article, the classification of cardiac abnormalities from electrocardiogram medical data has been carried out using the Fuzzy Cognitive Map (FCM) approach. FCM itself is a form of knowledge representation, design elements, and algorithm descriptions that are included in the FCM Expert software. The FCM model design can model complex systems. The results showed the real-time visualization of the normal heart error curve reached 16%, real-time visualization of the abnormal heart error curve reaches 31%, and the result of the convergence process of normal heart has the lowest convergence value of 0.39 and the highest convergence value of 0.91. Meanwhile, the result of abnormal heart convergence process has the lowest convergence value of 0.49 and the highest convergence value of 0.87. This research contributes to the world of health, where we classify the Electrocardiogram (ECG) data, so that it can classify abnormal and normal cardiac disorders using the Fuzzy Cognitive Map (FCM) algorithm.