Publication:
1st-degree Atrioventricular (AV-block) and Bundle Branch Block Prediction using Machine Learning

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dc.contributor.authorRasel, Risul Islam
dc.contributor.authorSultana, Nasrin
dc.contributor.authorMeesad, Phayung
dc.contributor.authorChowdhury, Anupam
dc.contributor.authorHossain, Meherab
dc.date.accessioned2023-12-16T10:55:15Z
dc.date.available2023-12-16T10:55:15Z
dc.date.issued2020
dc.date.issuedBE2563
dc.description.abstractHeart 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%.
dc.identifier.doi10.1145/3406601.3406647
dc.identifier.isbn978-1-4503-7759-1
dc.identifier.urihttps://harrt.in.th/handle/123456789/8402
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.publisher.placeKing Mongkut’s University of Technology Thonburi : Bangkok
dc.relation.ispartofseriesIAIT2020
dc.subjectการเรียนรู้ของเครื่อง
dc.subjectต้นไม้ตัดสินใจแบบจำแนก
dc.subjectต้นไม้ตัดสินใจแบบถดถอย
dc.subjectComputing Methodologies
dc.subjectMachine Learning
dc.subjectMachine Learning Approaches
dc.subjectClassification And Regression Trees
dc.subject.isced0322 บรรณารักษ์, สารสนเทศ และการศึกษาจดหมายเหตุ
dc.subject.oecd5.8 นิเทศศาสตร์และสื่อสารมวลชน
dc.title1st-degree Atrioventricular (AV-block) and Bundle Branch Block Prediction using Machine Learning
dc.typeเอกสารตีพิมพ์ในการประชุม (Conference Proceedings)
dspace.entity.typePublication
harrt.researchAreaสารสนเทศศาสตร์
harrt.researchGroupบรรณารักษศาสตร์และสารสนเทศศาสตร์
harrt.researchTheme.1Data Science
harrt.researchTheme.2Machine Learning
mods.location.urlhttps://doi.org/10.1145/3406601.3406647
oaire.citation.titleProceedings of the 11th International Conference on Advances in Information Technology
oairecerif.author.affiliationUniversity of Chittagong. Department of CSE
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