Analyzing the Impact of Data Resampling on Stroke Prediction using Machine Learning - Rahardi, Majid; Aminuddin, Afrig; Abdulloh, Ferian Fauzi; Asaddulloh, Bima Pramudya; Enriquez, Hesmeralda Rojas; Kusnawi, Kusnawi (Engineering, Technology & Applied Science Research, 2025)
This study focuses on stroke prediction using machine learning algorithms and evaluates the impact of different resampling techniques, including original, under-sampling, and over-sampling, on classification performance. The classifiers used in this study include Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and K-Nearest Neighbor (KNN). Each model was trained and evaluated using performance metrics such as accuracy, precision, recall, F1-score, and AUC. The results demonstrate that RF trained on the oversampled dataset achieved the best performance with an accuracy of 94.31%, a precision of 93.52%, a recall of 95.27%, an F1-score of 94.39%, and an AUC of 98.46% on the test set. These findings highlight the effectiveness of oversampling in handling imbalanced datasets and the superiority of RF in stroke prediction tasks compared to other classification methods and resampling techniques.