TY - GEN
T1 - Fuzzy C-Means-Grey Wolf Optimization for Classification of Stroke
AU - Setiawan, Qisthina Syifa
AU - Rustam, Zuherman
AU - Sa'id, Alva Andhika
AU - Maulidina, Faisa
AU - Sadewo, Wismaji
AU - Novkaniza, Fevi
N1 - Funding Information:
This research supported financially by FMIPA University of Indonesia with an FMIPA HIBAH 2021 research grant scheme.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Stroke is one of the diseases that affect humans, leading to death and disability. It occurs when some tissues in the brain die. This causes the blockage or rupture of blood vessels in the brain, which leads to damage in some of its parts. Therefore, parts of the body under the control of the damaged brain area cannot function properly. Ischemic and hemorrhagic stroke is the stroke main types. People with stroke require immediate treatment. However, the initial treatment is chosen based on the type of stroke that the patient has, whether ischemic or hemorrhagic. Therefore, it is essential to carry out stroke classification to administer appropriate initial treatment to patients. As technology grows, this can be carried out effectively and accurately using the machine learning method. In this study, the Fuzzy C-Means (FCM) with the Grey Wolf Optimization (GWO) feature selection (FCM-GWO) was implemented to classify the stroke dataset obtained from Dr. Cipto Mangunkusumo National General Hospital, Jakarta. Furthermore, the GWO feature selection was carried out on the stroke dataset to eliminate less important features and increase the performance of FCM. The results showed that the FCM-GWO and FCM achieved an accuracy value of 76% and 60%, respectively. Therefore, it was concluded that the FCM-GWO was able to increase the performance accuracy of FCM.
AB - Stroke is one of the diseases that affect humans, leading to death and disability. It occurs when some tissues in the brain die. This causes the blockage or rupture of blood vessels in the brain, which leads to damage in some of its parts. Therefore, parts of the body under the control of the damaged brain area cannot function properly. Ischemic and hemorrhagic stroke is the stroke main types. People with stroke require immediate treatment. However, the initial treatment is chosen based on the type of stroke that the patient has, whether ischemic or hemorrhagic. Therefore, it is essential to carry out stroke classification to administer appropriate initial treatment to patients. As technology grows, this can be carried out effectively and accurately using the machine learning method. In this study, the Fuzzy C-Means (FCM) with the Grey Wolf Optimization (GWO) feature selection (FCM-GWO) was implemented to classify the stroke dataset obtained from Dr. Cipto Mangunkusumo National General Hospital, Jakarta. Furthermore, the GWO feature selection was carried out on the stroke dataset to eliminate less important features and increase the performance of FCM. The results showed that the FCM-GWO and FCM achieved an accuracy value of 76% and 60%, respectively. Therefore, it was concluded that the FCM-GWO was able to increase the performance accuracy of FCM.
KW - Classification
KW - Feature Selection
KW - Fuzzy C-Means
KW - Grey Wolf Optimization
KW - Stroke
UR - http://www.scopus.com/inward/record.url?scp=85125783595&partnerID=8YFLogxK
U2 - 10.1109/DASA53625.2021.9682230
DO - 10.1109/DASA53625.2021.9682230
M3 - Conference contribution
AN - SCOPUS:85125783595
T3 - 2021 International Conference on Decision Aid Sciences and Application, DASA 2021
SP - 971
EP - 975
BT - 2021 International Conference on Decision Aid Sciences and Application, DASA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Decision Aid Sciences and Application, DASA 2021
Y2 - 7 December 2021 through 8 December 2021
ER -