The CLASSIFICATION OF STROKE PREDICTION USING THE SUPPORT VECTOR MACHINE (SVM) METHOD - Wulandari, Erika; Witanti, Arita (JATISI, 2024)
Stroke is a dangerous disease that can take someone's life, regardless of age. Several factors can cause a stroke, such as diabetes, hypertension, smoking, obesity, and other stroke factors. Therefore, understanding stroke is crucial for everyone to anticipate and prevent this disease. This stroke classification prediction research aims to determine the results of classification and the accuracy level of the data collected through the Support Vector Machine (SVM) method and SMOTE technique. Support Vector Machine (SVM) is an algorithm used to map information with minimal risk by separating hyperplanes. Before the testing phase, data balancing is also performed first using the SMOTE technique to ensure more accurate data processing. This research uses a dataset of 5,110 data points with 12 records. The classification results using the SVM method with the SMOTE technique yielded a good level of accuracy. Specifically, this research uses two ratios: 80:20 with an accuracy result of 85.45% and 70:30 with an accuracy result of 85.24%.