Lambert Aditama Soehardjianto

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...