Publication: 1st-degree Atrioventricular (AV-block) and Bundle Branch Block Prediction using Machine Learning
Submitted Date
Received Date
Accepted Date
Issued Date
2020
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
978-1-4503-7759-1
ISSN
eISSN
Scopus ID
WOS ID
Pubmed ID
arXiv ID
item.page.harrt.identifier.callno
Other identifier(s)
Journal Title
Proceedings of the 11th International Conference on Advances in Information Technology
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
1st-degree Atrioventricular (AV-block) and Bundle Branch Block Prediction using Machine Learning
Alternative Title(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
Heart block occurs when the flow of electricity interrupted or partially delayed between the top and bottom chambers of the heart. People are now more often affected by this kind of disease. However; early prediction of heart block can reduce the diagnosis complexity and treatment cost. In this study; a data mining and machine learning model is proposed to predict three types of heart blocks; such as 1st-degree A-V block; Left Bundle Branch Block (LBBB); and Right Bundle Branch Block (RBBB). Experiment data samples are collected from the cardiology department of Chittagong Medical College Hospital (CMCH); Bangladesh. The dataset contains 32 types of numeric and categorical features about the patient's ECG report; daily activities; and food habits. The prediction model has been designed; trained; and tested with some empirical machine learning algorithms namely Decision Tree; Random Forest; K-Nearest Neighbor; and Support Vector Machine. Finally; the experimentation shows that Decision Tree and Random Forest models outperform the other algorithms in overall heart block prediction with an accuracy of more than 92%.