Akademik Veri Yönetim Sistemi
3rd International Conference On Advanced Engineering, Technology And Applications, Catania, İtalya, 24 - 25 Mayıs 2024
Stroke is a condition characterized by the cessation of blood flow to a region of the brain or bleeding within the
brain. Early diagnosis and treatment not only reduce the risks of permanent damage and mortality but also enhance the
likelihood of recovery. Hence, timely diagnostic interventions are essential for formulating effective treatment strategies
and preventing potential complications. Machine learning models are frequently used in the literature as powerful tools in
stroke diagnosis. In this study, a comparative analysis of the effectiveness of methodologies used successfully in the
literature with the Extreme Gradient Boosting (XGBoost) machine learning method was conducted to overcome the
challenges caused by missing values and imbalanced datasets in stroke prediction. In the experiments, the Cerebral Stroke Prediction (CSP) dataset was employed to evaluate the performance of these methodologies using model evaluation
metrics. The study findings emphasize the effectiveness of SMOTEENN in addressing class imbalance and missing data
challenges across various imputation methods. This underlines the importance of employing suitable sampling and
imputation strategies to improve the performance of stroke prediction models.