Fuzzy kernel-based clustering and support vector machine algorithm in analyzing cerebral infarction dataset

Zuherman Rustam, Dea Aulia Utami, Jacub Pandelaki, Nadisa Karina Putri, Sri Hartini

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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%.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
PublisherSpringer
Pages1-11
Number of pages11
DOIs
Publication statusPublished - 1 Jan 2020

Publication series

NameLecture Notes in Networks and Systems
Volume123
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Keywords

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

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  • Cite this

    Rustam, Z., Utami, D. A., Pandelaki, J., Putri, N. K., & Hartini, S. (2020). Fuzzy kernel-based clustering and support vector machine algorithm in analyzing cerebral infarction dataset. In Lecture Notes in Networks and Systems (pp. 1-11). (Lecture Notes in Networks and Systems; Vol. 123). Springer. https://doi.org/10.1007/978-3-030-43002-3_1