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

Zuherman Rustam; Nadisa Karina Putri; Jacub Pandelaki; Widyo A. Nugroho; Dea A. Utami; Sri Hartini

doi:10.1007/978-3-030-43002-3_1

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

Zuherman Rustam, Nadisa Karina Putri, Jacub Pandelaki, Widyo A. Nugroho, Dea A. Utami, Sri Hartini

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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 language	English
Title of host publication	Lecture Notes in Networks and Systems
Publisher	Springer
Pages	1-11
Number of pages	11
ISBN (Electronic)	978-3-030-36664-3
DOIs	https://doi.org/10.1007/978-3-030-43002-3_1
Publication status	Published - 1 Jan 2020

Publication series

Name	Lecture Notes in Networks and Systems
Volume	123
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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10.1007/978-3-030-43002-3_1

Cite this

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title = "Fuzzy Kernel-Based Clustering and Support Vector Machine Algorithm in Analyzing Cerebral Infarction Dataset",

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{\textquoteright}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%.",

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)",

author = "Zuherman Rustam and Putri, {Nadisa Karina} and Jacub Pandelaki and Nugroho, {Widyo A.} and Utami, {Dea A.} and Sri Hartini",

note = "Funding Information: Acknowledgments. 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{\textquoteright}s Hospital. We thank to all reviewers for the improvement of this article. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.",

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Fuzzy Kernel-Based Clustering and Support Vector Machine Algorithm in Analyzing Cerebral Infarction Dataset. / Rustam, Zuherman; Putri, Nadisa Karina; Pandelaki, Jacub; Nugroho, Widyo A.; Utami, Dea A.; Hartini, Sri.

Lecture Notes in Networks and Systems. Springer, 2020. p. 1-11 (Lecture Notes in Networks and Systems; Vol. 123).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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

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