A Comparative Analysis of Machine Learning Models for Stroke Prediction

Abstract

Stroke is a leading global health burden, and there is an urgent need for improvement in risk prediction and treatment. This paper examines the capability of several machine learning algorithms, including Decision Trees, Random Forests, Neural Networks, Support Vector Machines (SVMs), Elastic Nets, and Lasso, to predict stroke risk on four cardiovascular and stroke datasets. The results indicate that Decision Trees and Random Forests are always better than Neural Networks, although Neural Networks show promising accuracy. SVMs are consistent, while the Elastic Net and Lasso models give average results.