TY - JOUR
T1 - Analyzing cerebral infarction using support vector machine with artificial bee colony and particle swarm optimization feature selection
AU - Rustam, Z.
AU - Utami, D. A.
AU - Pandelaki, J.
AU - Yunus, R. E.
N1 - Funding Information:
This research supported financially by the Ministry of Research and Higher Education Republic of Indonesia with PDUPT 2019 research grant scheme, ID number 1621/UN2.R3.1/HKP.05.00/2019. References [1] Wang, G., Jing, J., Pan, Y., Meng, X., Zhao, X., Liu, L., Li, H., Wang, D., & Wang, Y 2019 Does All Single Infarction Have Lower Risk of Stroke Recurrence Than Multiple Infarctions in Minor Stroke?. BMC Neurology, 19, 7 [2] Omar, H 2011 Myocardial Infarction Stroke Association International Journal of Cardiology, 154(3), 340 [3] Mentari, I., Naufalina, R., Rahmadi, M., Khotib, J 2018 Development of Ischemic Stroke Model by Right Unilateral Common Carotid Artery Occlusion (RUCCAO) Method Fol Med Indones, 54(3), 200-206 [4] Zhao, Lei., Biesbroek, J., Shi, L., Liu, W., Kujif, H., Chu, W., Abrigo, J., Lee, R., Leung, T., Lau, A., Biessels, G., Mok, V., & Wong, A 2017 Strategic Infarct Location for Post-Stroke Cognitive Impairment: A Multivariate Lesion-Symptom Mapping Study. Journal of Cerebral Blood Flow & Metabolism, 38(8), 1299-1311 [5] Xue, H., Yang, Q., & Chen, S 2009 SVM: Support Vector Machines United States of America: Taylor & Francis Group [6] Rustam,Z., Yaurita,F 2018 Insolvency Prediction in Insurance Companies Using Support Vector Machines and Fuzzy Kernel C-Means Journal of Physics: Conference Series Vol.1028(1) [7] Panca,V., Rustam,Z 2016 Application of Machine Learning on Brain Cancer Multiclass Classification AIP Conference Procedings, Vol.1862 [8] Hala, M 2018 Co-ABC: Correlation Artificial Bee Colony Algorithm for Biomarker Gene Discovery using gene Expression Profile Saudi Journal of Biological Sciences 25, 895-903 [9] Indu, J., Vinod, K., Renu, J 2018 Correlation Feature Selection Based Improved-Binary Particle Swarm Optimization for Gene Selection and Cancer Classification Applied Soft Computing Vol. 62, 203-215 [10] Beatriz, A., Katya, R., Roberto, V 2016 Classification of DNA Microarrays using Artificial Neural Networks and ABC Algorithm Applied Soft Computing 38, 548-560 [11] Ezgi, Z., Selma, A 2016 A Hybrid Approach of Differential Evolution and Artificial Bee Colony for Feature Selection Expert Systems With Applications 62, 91-103 [12] Laith, M., Ahamad, T 2017 Unsupervised Text Feature Selection Technique Based on Hybrid Particle Swarm Optimization Algorithm with Genetic Operators for the Text Clustering J Supercomput 73, 4773-4795 [13] Ali, A., Ali B 2018 Image Steganalysis using Improved Particle Swarm Optimization Based Feature Selection Appl Intell 48, 1609-1622 [14] Wang, L 2005 Support Vector Machines: Theory and Applications. New York: Springer, 177
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2020/6/9
Y1 - 2020/6/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85088139868&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1490/1/012031
DO - 10.1088/1742-6596/1490/1/012031
M3 - Conference article
AN - SCOPUS:85088139868
SN - 1742-6588
VL - 1490
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012031
T2 - 5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019
Y2 - 19 October 2019
ER -