Stroke Disease Prediction Based on Multi-Model Ensemble Learning

Cui, Xiaojian

doi:10.1109/ainit61980.2024.10581532

2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) 2024

DOI: 10.1109/ainit61980.2024.10581532

|View full text |Cite

Stroke Disease Prediction Based on Multi-Model Ensemble Learning

Xiaojian Cui

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2025

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

An Ensemble Cognitive Model for Stroke Prediction Using Unstructured Health Information Powered by Machine Learning

Ghanimi,

et al. 2025

JMC

View full text Add to dashboard Cite

Machine Learning (ML) algorithms have procured a profound position in healthcare sectors, especially in diagnosis, treatments, and recommendation systems. The ML is evolving as an aiding tool for medical practitioners in disease diagnosis. Also, the feature selection reveals the latent relationships among the features, which emerge significant scope for clinical research. In the proposed study, a cognitive ensemble model (CEM) was developed to predict the probability of stroke among various subjects using highly raw clinical data. The optimal base learners are made in such a way that each of them complements one another. The proposed CEM is tested on a real-world dataset on important classification metrics. The results indicate that the CEM deployed in the healthcare sector forewarns patients regarding the probability of stroke.

show abstract

An Ensemble Cognitive Model for Stroke Prediction Using Unstructured Health Information Powered by Machine Learning

Ghanimi,

et al. 2025

JMC

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Stroke Disease Prediction Based on Multi-Model Ensemble Learning

Cited by 1 publication

References 12 publications

An Ensemble Cognitive Model for Stroke Prediction Using Unstructured Health Information Powered by Machine Learning

An Ensemble Cognitive Model for Stroke Prediction Using Unstructured Health Information Powered by Machine Learning

Contact Info

Product

Resources

About