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

TL;DR

This paper presents an interpretable machine learning framework that combines ensemble models and explainable AI techniques to achieve highly accurate stroke risk prediction, aiding early intervention and personalized treatment.

Contribution

It introduces a novel ensemble modeling approach with XAI for stroke risk prediction, enhancing accuracy and interpretability over existing methods.

Findings

Achieved 99.09% accuracy on the Stroke Prediction Dataset

Identified key clinical variables: age, hypertension, glucose levels

Demonstrated the effectiveness of combining ensemble learning with XAI

Abstract

Stroke is a major cause of death and permanent impairment, making it a major worldwide health concern. For prompt intervention and successful preventative tactics, early risk assessment is essential. To address this challenge, we used ensemble modeling and explainable AI (XAI) techniques to create an interpretable machine learning framework for stroke risk prediction. A thorough evaluation of 10 different machine learning models using 5-fold cross-validation across several datasets was part of our all-inclusive strategy, which also included feature engineering and data pretreatment (using Random Over-Sampling (ROS) to solve class imbalance). Our optimized ensemble model (Random Forest + ExtraTrees + XGBoost) performed exceptionally well, obtaining a strong 99.09% accuracy on the Stroke Prediction Dataset (SPD). We improved the model's transparency and clinical applicability by identifying three important clinical variables using LIME-based interpretability analysis: age, hypertension, and glucose levels. Through early prediction, this study highlights how combining ensemble learning with explainable AI (XAI) can deliver highly accurate and interpretable stroke risk assessment. By enabling data-driven prevention and personalized clinical decisions, our framework has the potential to transform stroke prediction and cardiovascular risk management.