Development and Evaluation of Stroke Disease Classification Models: Classical Machine Learning, Deep Learning, and Explainable AI Approaches - Lianny Wydiastuty Kusuma; Andri Wijaya; Asahiro Nathanael Star Sitohang; Ceng Giap Yo (Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto, 2025)
This study evaluates the impact of the Synthetic Minority Oversampling Technique (SMOTE) on improving machine learning and deep learning performance in stroke risk classification using secondary, publicly available data from Kaggle’s Stroke Prediction Dataset (n = 5,110; 249 stroke cases, 4,861 non-stroke cases), for deep learning. Performance was measured using accuracy, precision, recall, and F1-score, while Explainable AI (XAI) methods (SHAP, LIME) were utilized for interpretability. The results show that applying SMOTE improves the model's sensitivity to the minority