<![CDATA[Stroke is a non-communicable disease with high mortality and disability rates, necessitating a classification approach that can facilitate more effective detection. Class imbalance in stroke datasets causes classification models to be biased toward the majority class, resulting in suboptimal classification performance. This study aims to analyze the performance of Recursive Feature Elimination (RFE) in a Support Vector Machine (SVM) model with data imbalance handling using Adaptive Synthetic Sampling (ADASYN) in stroke classification. The dataset used is a secondary dataset from Kaggle consisting of 5109 data points after the preprocessing stage. The modeling process was conducted by testing various data split ratios as well as combinations of kernels and SVM parameters using a 5-fold cross-validation approach. The results show that the best model was obtained with an 80:20 split ratio, a polynomial kernel, and a C parameter of 0.1, yielding an accuracy of 0.75, precision of 0.14, recall of 0.82, an F1-score of 0.24, and an AUC of 0.8245. The application of RFE resulted in improved model performance compared to without RFE, although the magnitude of the improvement was relatively small. The still low precision value indicates that the model still produces many false positives, so the classification challenge on the stroke dataset has not been fully resolved. On the other hand, an AUC value of 0.8245 indicates that the model performs reasonably well in distinguishing between the two classes overall, although its application in a clinical context still requires further refinement.]]>
