Application of Fuzzy Kernel K-Medoids Method for Cancer Classification based on Metal Concentration in Blood



Classification technique has already been applied widely in the medical data. One of its applications is for classification of cancer. The accuracy of this technique highly depends on the type of data to be processed (whether the data are separable or non-separable) and the dissimilarity function used. To surmount those hindrances and to improve the accuracy of classification therefore a method named Fuzzy Kernel K-Medoids (FKKM). The method can be used for separable or non separable of data. Based on the research on the concentration data of Zn, Ba, Mg, Ca, Cu, and Se in blood in order to diagnose cancer, FKKM gives better result than the Support Vector Machines Method. This paper will discuss an application of the FKKM method on the concentration data of Zn, Ba, Mg, Ca, Cu, and Se in blood samples and compared with the Support Vector Machines Method for the diagnosis of cancer. Results showed that the FKKM method produced a better result than the Support Vector Machines Method.


1. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek., Mesirov JP, et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science.1999. 286: 531-37.

2. Sumati V, Singh L, Kumar S. Application of volutionary fuzzy neural network in predicting cancer subgroups. XXXII National Systems ConferenceNSC. 2008. 37-42.

3. Fikri A, Rustam Z, Pandelaki J Brain cancer (Astrocytoma) clustering menggunakan fuzzy C-Means. Prosiding Seminar Nasional Matematika 2010. Depok,4 Februari 2010: 271-7.

4. Wibowo AP , Rustam Z, Pandelaki J. Clustering brain cancer menggunakan possibilitik c-means. Prosiding Seminar Nasional Matematika 2010,Depok. 2010: 289-92.

5. Krismanti A, Rustam Z, Pandelaki J. Aplikasi spherical k-means pada pengklasifikasian brain cancer. Prosiding Seminar Nasional Matematika 2010,Depok. 2010:293-7.

6. Dehzangi A, Phon-Amnuaisuk S, and Dehzangi O. Using random forest for protein fold prediction problem: An empirical study. J Information Science and Engineering. 2010. (26): 1941-56.

7. Andonie R, Fabry-Asztalos L, Abdul-Wahid C.B. Abdul-Wahid S, Barker GI, Magill LC. Fuzzy ARTMAP prediction of biological activities for potential HIV-1 protease inhibitors using a small molecular data set. EEE/ACM Transactions on Computational Biology and Bioinformatics. 2011. 8(1): 80-93.

8. Bhasin M and Raghava GPS. GPCR pred: an SVMbased method for prediction of families and subfamilies of G-protein coupled receptors, Nucleic Acids Research. 2004. (32): 383-9.

9. Cai CZ, Han LY, Ji ZL, Chen X and Chen YZ. SVMProt: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 2003. (31): 3692-7.

10. Cai CZ, Han LY, Ji ZL. and Chen YZ. Enzyme family classification by support vector machines. Proteins. 2004. (55): 66-76.

11. Patrizi G, Cifarelli C, Losacco V, and Patrizi G. Secondary structure classification of isoform protein markers in oncology, Mathematical Approaches to Polymer Sequence Analysis and Related Problems 2011. 7-67.

12. Ivanciuc O. Support vector mchines for cancer diagnosis from the Blood Concentration of Zn, Ba, Mg, Ca, Cu, and Se. Internet Electronic Journal of Molecular Design. 2002. (I): 418-27.

13. Rustam Z . Algoritma Fuzzy Kernel K-Medoids untuk klasifikasi data multikelas. Prosiding Seminar Nasional Teknologi Informasi 2010. Universitas Tarumanagara Jakarta. 2010. 75-9.

14. Krishnapuram R, Joshi A, and Yi L. A fuzzy relative of the medoids algorithm with application to web document and snippet clustering. EEE International Fuzzy Systems Conference, Seoul, Korea. 1999. 1281-6.
How to Cite
RUSTAM, ZUHERMAN; AZIZ, ZUHELMI. Application of Fuzzy Kernel K-Medoids Method for Cancer Classification based on Metal Concentration in Blood. JURNAL ILMU KEFARMASIAN INDONESIA, [S.l.], v. 9, n. 2, p. 147-151, sep. 2011. ISSN 2614-6495. Available at: <>. Date accessed: 29 may 2023.