Publication: Predicting stroke by combination of sequence pattern Mining and associative classification
Submitted Date
Received Date
Accepted Date
Issued Date
2028
Copyright Date
Announcement No.
Application No.
Patent No.
Valid Date
Resource Type
Edition
Resource Version
Language
en
File Type
No. of Pages/File Size
ISBN
ISSN
eISSN
Scopus ID
WOS ID
Pubmed ID
arXiv ID
item.page.harrt.identifier.callno
Other identifier(s)
Journal Title
2018 International Conference on Information Technology (InCIT)
Volume
Issue
Edition
Start Page
End Page
Access Rights
Access Status
Rights
Rights Holder(s)
Physical Location
Bibliographic Citation
Research Projects
Organizational Units
Authors
Journal Issue
Title
Predicting stroke by combination of sequence pattern Mining and associative classification
Alternative Title(s)
Author(s)
Author’s Affiliation
Author's E-mail
Editor(s)
Editor’s Affiliation
Corresponding person(s)
Creator(s)
Compiler
Advisor(s)
Illustrator(s)
Applicant(s)
Inventor(s)
Issuer
Assignee
Other Contributor(s)
Series
Has Part
Abstract
Stroke is a medical emergency that needs immediate medical attention. It is the third cause of death in the world and is the first cause of death of elderly women in Thailand. Stroke needs to be predicted in order to prevent people from the disease and to prepare proper medical treatments for the patients. A number of research works tried to study factors; such as blood pressure; smoking; and cholesterol; for predicting stroke. Unlike the previous works; the association of disease sequence is combined with factors for predicting stroke in this paper. The association is represented in the form of class sequential rules which demonstrate the association of diseases and factors leading to stroke. The combination of sequential pattern mining and associative classification is proposed as a method for generating class sequential rules. The experimental results show that the proposed technique gives high performance for the prediction. In addition; this paper shows top ten association of the disease and factors leading to stroke.