Comparative Analysis of Stroke Prediction Models Using Machine Learning
It addresses stroke prediction for healthcare applications, but is incremental as it compares existing methods without major breakthroughs.
This study tackled stroke risk prediction using machine learning on demographic, clinical, and lifestyle data, finding that models like Logistic Regression, Random Forest, and XGBoost achieve high accuracy but have limited sensitivity for clinical use.
Stroke remains one of the most critical global health challenges, ranking as the second leading cause of death and the third leading cause of disability worldwide. This study explores the effectiveness of machine learning algorithms in predicting stroke risk using demographic, clinical, and lifestyle data from the Stroke Prediction Dataset. By addressing key methodological challenges such as class imbalance and missing data, we evaluated the performance of multiple models, including Logistic Regression, Random Forest, and XGBoost. Our results demonstrate that while these models achieve high accuracy, sensitivity remains a limiting factor for real-world clinical applications. In addition, we identify the most influential predictive features and propose strategies to improve machine learning-based stroke prediction. These findings contribute to the development of more reliable and interpretable models for the early assessment of stroke risk.