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.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 9 Jun 2020|
|Event||5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019 - Surabaya, Indonesia|
Duration: 19 Oct 2019 → …