Akademik Veri Yönetim Sistemi
COMPUTING AND INFORMATICS, cilt.44, sa.5, ss.1101-1122, 2025 (SCI-Expanded, Scopus)
A stroke is a serious neurological condition that occurs due to eitherblockages or bleeding in the brain, which can lead to death or long-term disability.This study aims to enhance the accuracy of disease diagnosis in imbalanced strokepatient datasets. The model incorporates an artificial immune system algorithm,whose parameters are fine-tuned using the Firefly algorithm to ensure both stability and balanced data. To enhance the performance of the underrepresented class, theOne-Sided Selection method is employed. The model’s effectiveness was tested intwo separate experiments: one utilizing all available features and the other apply-ing the Artificial Bee Colony (ABC) algorithm to select the most relevant features.The models were trained using six different classification algorithms: CatBoost,Light Gradient Boosting Machine (LightGBM), Gradient Boosting (GB), ExtremeGradient Boosting (XGBoost), Support Vector Machine (SVM), and Logistic Re-gression (LR). The results were presented using performance metrics. When trainedusing all features, the model achieved an accuracy of 93 %, specificity of 93 %, andsensitivity of 80 %. When trained using the best features selected by the ABC algo-rithm, the model achieved an accuracy of 93 %, specificity of 93 %, and sensitivityof 82 %. Compared to previous studies, the proposed model was effective in bothexperiments