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

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@article{IJASEIT9580,
   author = {Zuherman Rustam and Annisa Kamalia and Rahmat Hidayat and Fajar Subroto and Aditya Suryansyah S},
   title = {Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {4},
   year = {2019},
   pages = {1205--1210},
   keywords = {thalassemia; fuzzy C-means; fuzzy kernel C-means; fuzzy robust C-means; fuzzy kernel robust C-means.},
   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.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9580},    doi = {10.18517/ijaseit.9.4.9580} }

EndNote

%A Rustam, Zuherman
%A Kamalia, Annisa
%A Hidayat, Rahmat
%A Subroto, Fajar
%A Suryansyah S, Aditya
%D 2019
%T Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data
%B 2019
%9 thalassemia; fuzzy C-means; fuzzy kernel C-means; fuzzy robust C-means; fuzzy kernel robust C-means.
%! Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data
%K thalassemia; fuzzy C-means; fuzzy kernel C-means; fuzzy robust C-means; fuzzy kernel robust C-means.
%X 

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.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9580 %R doi:10.18517/ijaseit.9.4.9580 %J International Journal on Advanced Science, Engineering and Information Technology %V 9 %N 4 %@ 2088-5334

IEEE

Zuherman Rustam,Annisa Kamalia,Rahmat Hidayat,Fajar Subroto and Aditya Suryansyah S,"Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 4, pp. 1205-1210, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.4.9580.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Rustam, Zuherman
AU  - Kamalia, Annisa
AU  - Hidayat, Rahmat
AU  - Subroto, Fajar
AU  - Suryansyah S, Aditya
PY  - 2019
TI  - Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 4
Y2  - 2019
SP  - 1205
EP  - 1210
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - thalassemia; fuzzy C-means; fuzzy kernel C-means; fuzzy robust C-means; fuzzy kernel robust C-means.
N2  - 

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.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9580 DO - 10.18517/ijaseit.9.4.9580

RefWorks

RT Journal Article
ID 9580
A1 Rustam, Zuherman
A1 Kamalia, Annisa
A1 Hidayat, Rahmat
A1 Subroto, Fajar
A1 Suryansyah S, Aditya
T1 Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 4
YR 2019
SP 1205
OP 1210
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 thalassemia; fuzzy C-means; fuzzy kernel C-means; fuzzy robust C-means; fuzzy kernel robust C-means.
AB 

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.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9580 DO - 10.18517/ijaseit.9.4.9580