Conference
Aug 2026
Learning from Imbalanced Stroke Data: A Comparative Study of SMOTE and SMOTE-ENN on Structured Health Records
Stroke prediction with machine learning is challenged by severe class imbalance, which can produce models that appear accurate but fail to detect true stroke cases. This study evaluates Logistic Regression, SVM, and XGBoost on the Kaggle Stroke Prediction Dataset under three training regimes: no resampling, SMOTE, and...
Lambert Aditama Soehardjianto, Wilsen Soetresno, Erna Fransisca Angela Sihotang et al.
· International Conferences on... · 0 citations