Comparative Analysis of Stroke Prediction Models Using Machine Learning

TL;DR

This study compares various machine learning models for stroke prediction, highlighting their performance, limitations, and key features, aiming to enhance early risk assessment in clinical settings.

Contribution

It provides a comprehensive evaluation of multiple ML algorithms on stroke prediction, addressing methodological challenges and identifying influential features for improved models.

Findings

Random Forest and XGBoost outperform Logistic Regression in accuracy.

Sensitivity remains a challenge for clinical applicability.

Key predictive features include age, blood pressure, and lifestyle factors.

Abstract

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.