Analyzing cerebral infarction using support vector machine with artificial bee colony and particle swarm optimization feature selection

Z. Rustam, D. A. Utami, J. Pandelaki, R. E. Yunus

Research output: Contribution to journalConference article

Abstract

Early diagnosis of cerebral infarction is essential since many patients cannot be cured where the diagnosis is made at an advanced stage. In case an infarct occurs, the tissue in the brain die and stop the circulation of blood, which carries oxygen and nutrients to the body. Therefore, this study uses a machine learning Support Vector Machine (SVM) for early detection of the disorder. To produce the best classification accuracy and fast computing time, feature selection is performed on cerebral infarction data, including Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). After classification, infarction data with the best features are classified using SVM. The classification results of ABC-SVM and PSO-SVM methods are compared with the accuracy of 90.36% for ABC-SVM and 86.74% for PSO-SVM. Therefore, the best approach used in classification is the SVM method with ABC feature selection.

Original languageEnglish
Article number012031
JournalJournal of Physics: Conference Series
Volume1490
Issue number1
DOIs
Publication statusPublished - 9 Jun 2020
Event5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019 - Surabaya, Indonesia
Duration: 19 Oct 2019 → …

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