Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data

Abstract

Among the inherited blood disorders in Southeast Asia, thalassemia is the most prevalent. Thalassemias are pathologies that derive from genetic defects of the globin genes. Thalassemia is also considered a health burden among the world’s population. Thalassemia cannot be cured, but there is a method to prevent the occurrence of thalassemia by early detection with  screening. The aim is to identify the suspected unrecognised diseases in a population that seems healthy and asymptomatic using tests, examinations, or other procedures that can be applied quickly and easily to the target population. Research on thalassemia has been done extensively, such as testing the accuracy of β-thalassemia data in Thailand using the Bayesian Network and Multinomial Logistic Regression. In this study, we will compare the performance of the classification of thalassemia data by Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means. The author uses thalassemia data from Indonesia, acquired from Harapan Kita Children and Womens’s Hospital,  Jakarta, that consists of 82 thalassemia samples from the patients of thalassemia and 68 non-thalassemia samples with 11 features. In total, there are 150 data patients used in this paper. The results show the accuracy of the classification. The accuracy of FCM is 100% when training data is 90%, FRCM is 100% when training data is 90%, and FKRCM, which is the modified Fuzzy, 100% when we use the and 80% & 90% training data. This result denote that Fuzzy C-Means, Fuzzy Robust C-Means, and Fuzzy Kernel Robust C-Means perfectly classify thalassemia data from Indonesia.