Application of the Fuzzy C-Means Method in Grouping Heart Abnormalities Based on Electrocardiogram Medical Records
Heart disease is the main cause of death which can be diagnosed using an electrocardiogram. This study aims to classify heart defects using the Fuzzy C Means technique. The advantage of using Fuzzy C Means is that it is unsupervised and can reach a convergent cluster center under certain conditions....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Fakultas Ilmu Komputer UMI
2023-04-01
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| Series: | Ilkom Jurnal Ilmiah |
| Subjects: | |
| Online Access: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1272 |
| Summary: | Heart disease is the main cause of death which can be diagnosed using an electrocardiogram. This study aims to classify heart defects using the Fuzzy C Means technique. The advantage of using Fuzzy C Means is that it is unsupervised and can reach a convergent cluster center under certain conditions. It is a clustering model that has the value of the objective function, number of iterations and completed time. In an unsupervised learning, the focus is more on exploring data such as looking for patterns in the data. Clustering itself aims to identify patterns of similar data to be grouped. It can be a solution to overcome the process of determining the risk of heart disease. The results showed that there were 10 data grouped into cluster 1 and 10 data into cluster 2. The first group (Cluster 1) consisted of patients with serial numbers 3,5,8,9,11,12,16,17,19,20, while the second group (Cluster 2) consisted of patients with serial numbers 1,2,4,6,7,10,13,14,15 and 18. Accuracy testing results in a success rate of 60%. |
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| ISSN: | 2087-1716 2548-7779 |