Fuzzy Kernel-Based Clustering and Support Vector Machine Algorithm in Analyzing Cerebral Infarction Dataset

  • Zuherman Rustam
  • Dea Aulia UtamiEmail author
  • Jacub Pandelaki
  • Nadisa Karina Putri
  • Sri Hartini
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 123)


Ischemic stroke is a disease that occurs due to disruption of blood circulation to the brain due to blood clots in the brain. The blockage is called cerebral infarction. In diagnosing the presence of cerebral infarction in the brain, machine learning is used because it is not enough just to use a CT scan to diagnose. To deal with the problem of classification of cerebral infarction data obtained from Dr. Cipto Mangunkusumo’s Hospital in Jakarta, this study proposes the use of Fuzzy C-Means Clustering (FCM), Fuzzy Possibilistic C-Means (FPCM), and Radial Base Function Fuzzy Possibilistic C-Means (RBFFPCM) method as a clustering method and a Support Vector Machine (SVM) method as a classification method. This method will be compared to the level of accuracy. The greatest level of accuracy is generated from the Radial Base Function Fuzzy Possibilistic C-Means (RBFFPCM) method with an accuracy value of 91%.


Fuzzy C-Means Clustering (FCM) Fuzzy Possibilistic C-Means (FPCM) Radial Base Function Fuzzy Possibilistic C-Means (RBFFPCM) Support Vector Machine (SVM) Cerebral infarction Ischemic stroke 



This work was financially supported by The Indonesian Ministry of Research and Higher Education, under Grant PDUPT 2019 (ID number NKB-1621/UN2.R3.1/HKP05.00/2019). This work supported by Department Radiology of Dr. Cipto Mangunkusumo’s Hospital. We thank to all reviewers for the improvement of this article.


  1. 1.
    Kementrian Kesehatan Republik Indonesia. Accessed 7 Mar 2019
  2. 2.
    World Health Organization (WHO). Accessed 7 Mar 2019
  3. 3.
    Mentari, I.A., Naufalina, R., Rahmadi, M., Khotib, J.: Development of ischemic stroke model by right unilateral common carotid artery occlusion (RUCCAO) Method. Fol Med Indones 54(3), 200–206 (2018)CrossRefGoogle Scholar
  4. 4.
    Bay, V., Kjolby, B.F., Iversen, N.K., Mikkelsen, I.K., Ardalan, M., Nyengaard, J.R., Jespersen, S.N., Drasbek, K.R., Stergaard, L., Hansen, B.: Stroke infarct volume estimation in fixed tissue : comparison of diffusion kurtosis imaging to diffusion weighted imaging and histology in a rodent MCAO model. PLoS ONE 13(4), e0196161 (2018)CrossRefGoogle Scholar
  5. 5.
    Havens, T.C., Bezdek, J.C., Leckie, C., Palaniswami, M.: Fuzzy c-means algorithms for very large data. IEEE Trans. Fuzzy Syst. 20(6), 1130–1146 (2012)CrossRefGoogle Scholar
  6. 6.
    Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.: A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst. 13(4), 517–530 (2005)CrossRefGoogle Scholar
  7. 7.
    Izakian, H., Abraham, A.: Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst. Appl. 38, 1835–1838 (2011)CrossRefGoogle Scholar
  8. 8.
    Kannan, S.R., Devi, R., Ramathilagam, S., Hong, T.P.: Effective fuzzy possibilistic c-means: an analyzing cancer medical database. Soft. Comput. 21, 2835–2845 (2017)CrossRefGoogle Scholar
  9. 9.
    Dsouza, K.J., Ansari, Z.A.: Experimental exploration of support vector machine for cancer cell classification. In: IEEE International Conference on Cloud Computing in Emerging Markets (2017)Google Scholar
  10. 10.
    Saad, M.F., Salah, M., Lee, J., Kwon, O.: A modified fuzzy possibilistic C-means for context data clustering toward efficient context prediction. In: New Challenges for Intelligent Information SCI, vol. 351, pp. 157–165 (2011)Google Scholar
  11. 11.
    Zhang, C., Zhou, Y., Martin, T.: Similarity based fuzzy and possibilistic c-means algorithm. In: Proceedings of the 11th Joint Conference on Information Sciences (2008)Google Scholar
  12. 12.
    Kannan, S.R., Devi, R., Ramathilagam, S., Hong, T.P., Ravikumar, A.: Robust fuzzy clustering algorithms in analyzing high dimensional cancer databases. J. Appl. Soft Comput. 35, 199–213 (2015)CrossRefGoogle Scholar
  13. 13.
    Rustam, Z., Talita, A.S.: Fuzzy Kernel C-means algorithm for intrusion detection systems. J. Theor. Appl. Inf Technol. 81(1), 161 (2015)Google Scholar
  14. 14.
    Rustam, Z., Talita, A.S.: Fuzzy Kernel K-medoids algorithm for multiclass multidimensional data classification. J. Theor. Appl. Inf Technol. 80(1), 147 (2015)Google Scholar
  15. 15.
    Liu, J., Zio, E.: Integration of feature vector selection and support vector machine for classification of imbalanced data. Appl. Soft Comput. J. 75, 702–711 (2017)CrossRefGoogle Scholar
  16. 16.
    Mathew, J., Pang, C.K., Luo, M., Leong, W.H.: Classification of imbalanced data by oversampling in kernel space of support vector machine. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 4065–4076 (2018)CrossRefGoogle Scholar
  17. 17.
    Wang, H., Zheng, B., Yoon, S.W., Ko, H.S.: A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur. J. Oper. Res. 267, 687–699 (2018)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zuherman Rustam
    • 1
  • Dea Aulia Utami
    • 1
    Email author
  • Jacub Pandelaki
    • 2
  • Nadisa Karina Putri
    • 1
  • Sri Hartini
    • 1
  1. 1.Department of MathematicsFMIPA Universitas IndonesiaDepokIndonesia
  2. 2.Medical Department of RadiologyRSUPN Dr. Cipto MangunkusumoKota Jakarta Pusat, DKI, JakartaIndonesia

Personalised recommendations