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 either blockages 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 stroke patient 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, the One-Sided Selection method is employed. The model's effectiveness was tested in two 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), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Logistic Re-gression (LR). The results were presented using performance metrics. When trained using all features, the model achieved an accuracy of 93%, specificity of 93%, and sensitivity 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 sensitivity of 82%. Compared to previous studies, the proposed model was effective in both experiments.