Comparison of Xgboost, Random Forest and Logistic Regression Algorithms in Stroke Disease Classification - Sitompul, Lia Relita; Nababan, Adli Abdillah; Manihuruk, Mey Lestari; Ponsen, Wildan Andika; Supriyandi (Sinkron : jurnal dan penelitian teknik informatika, 2025)
Stroke remains a critical global health concern, ranking as the second leading cause of mortality and third cause of disability worldwide. Early detection and accurate classification of stroke risk could significantly improve patient outcomes through timely interventions. This research evaluates and compares the performance of three machine learning algorithms—XGBoost, Random Forest, and Logistic Regression—for stroke disease classification using a dataset of 5,110 patient records with 12 attributes including demographic, lifestyle, and health factors. Due to significant data imbalance between stroke and non-stroke cases, Synthetic Minority Over-sampling Technique (SMOTE) was applied to enhance model performance. Comprehensive evaluation metrics including accuracy, precision, recall, and F1-score were utilized to assess each algorithm's effectiveness. Results demonstrate that XGBoost achieved superior performance with 95% accuracy, followed by Random Forest at 94% and Logistic Regression at 82%. Feature importance analysis identified age, average blood glucose level, and history of heart disease as the most significant predictors for stroke diagnosis. This study contributes to the advancement of clinical decision support systems by highlighting the effectiveness of ensemble learning approaches for stroke prediction, potentially enabling earlier interventions and improved patient management. These findings suggest that integration of machine learning tools in clinical settings could enhance stroke risk assessment, though further validation with diverse patient populations is recommended for broader implementation.