Open AccessAnalyzing cerebral infarction using support vector machine with artificial bee colony and particle swarm optimization feature selection
Author(s) -
Zuherman Rustam,
Dea Aulia Utami,
Jacub Pandelaki,
Reyhan Eddy Yunus
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1490/1/012031
Subject(s) - support vector machine , feature selection , particle swarm optimization , artificial intelligence , pattern recognition (psychology) , computer science , selection (genetic algorithm) , feature (linguistics) , feature vector , machine learning , philosophy , linguistics
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.