Kernel-based fuzzy clustering for sinusitis dataset

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

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Sinusitis is a condition resulting from inflammation of sinus walls. In handling the disease, machine learning method is often used to find more precise and accurate treatment plan for patients. For instance, fuzzy clustering is widely used for pattern recognition and data mining. Due to uncertainty and ambiguity, this method is used to overcome the non-linearity of medical dataset. In this study, fuzzy clustering was provided with kernel methods. We used some Kernel methods such as, Kernelized Fuzzy c-Means (KFCM), Kernelized Possibilistic c-Means (KPCM), Kernelized Fuzzy Possibilistic c-Means (KFPCM), and Kernelized Possibilistic Fuzzy c-Means (KPFCM) for clustering sinusitis dataset. The dataset was retrieved from Cipto Mangunkusumo Hospital Jakarta, Indonesia, which contains 4 features and 200 instances of this condition. These level of accuracy and model performance are used to compare these approaches. The result showed that KFCM has the highest accuracy for categorizing sinusitis dataset with accuracy of 96.97% and running time of 0.01 s.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
PublisherSpringer
Pages44-54
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

  • Kernelized Fuzzy c-Means (KFCM)
  • Kernelized Fuzzy Possibilistic c-Means (KFPCM)
  • Kernelized Possibilistic c-Means (KPCM)
  • Kernelized Possibilistic Fuzzy c-Means (KPFCM)
  • Sinusitis

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  • Cite this

    Rustam, Z., Putri, N. K., Pandelaki, J., Nugroho, W. A., Utami, D. A., & Hartini, S. (2020). Kernel-based fuzzy clustering for sinusitis dataset. In Lecture Notes in Networks and Systems (pp. 44-54). (Lecture Notes in Networks and Systems; Vol. 123). Springer. https://doi.org/10.1007/978-3-030-43002-3_5