The comparison study of kernel KC-means and support vector machines for classifying schizophrenia

Sri Hartini, Zuherman Rustam

Research output: Contribution to journalArticle


Schizophrenia is one of mental disorder that affects the mind, feeling, and behavior. Its treatment is usually permanent and quite complicated; therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the experiments, kernel KC-means with the sixth-order polynomial kernel gives 87.18 percent accuracy and 93.15 percent F1-score at the faster running time than support vector machines. However, with the same kernel, it was further deduced from the results that support vector machines provides better performance with an accuracy of 88.78 percent and F1-score of 94.05 percent.

Original languageEnglish
Pages (from-to)1643-1649
Number of pages7
JournalTelkomnika (Telecommunication Computing Electronics and Control)
Issue number3
Publication statusPublished - 1 Jun 2020


  • Fast fuzzy clustering
  • KC-means
  • Kernel function
  • Schizophrenia classification
  • Support vector machines

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