Optimizing Stroke Risk Prediction: A Machine Learning Pipeline Combining ROS-Balanced Ensembles and XAI

A new machine learning framework has been developed to optimize stroke risk prediction, utilizing ensemble modeling and explainable AI techniques. This framework achieved a remarkable accuracy of 99.09% on the Stroke Prediction Dataset by employing Random Over-Sampling to address class imbalance and identifying key clinical variables through LIME-based methods.
This advancement is significant as it enhances the ability to predict stroke risk, potentially leading to earlier interventions and improved patient outcomes. The model's transparency and clinical applicability make it a valuable tool for healthcare professionals.
The development of this framework reflects a growing trend in healthcare towards integrating advanced machine learning techniques for risk assessment across various medical conditions. Similar methodologies are being explored in other areas, such as cancer risk stratification and injury prediction in athletes, highlighting the versatility and importance of machine learning in modern medicine.

Optimizing Stroke Risk Prediction: A Machine Learning Pipeline Combining ROS-Balanced Ensembles and XAI