Year: 2025
Type: article
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.
DOI: 10.34256/ijcci2514
Authors: Kripa Mary Jose, Nizar Banu, Melvin Infant A
Institution: Christ University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4412732244