Classification of premature cardiac contractions based on RFECV and ensemble learning.
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| Title: | Classification of premature cardiac contractions based on RFECV and ensemble learning. |
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| Authors: | Nurdiniyah, Elsa Sari Hayunah1 elsa.nurdiniyah@unsoed.ac.id, Yusuf, A'isya Nur Aulia1 aisya.yusuf@unsoed.ac.id, Amalia, Norma1 norma.amalia@unsoed.ac.id, Purnomo, Widhiatmoko Herry1 widhiatmoko.purnomo@unsoed.ac.id, Hafizha, Azizah Najda1 azizah.hafizha@mhs.unsoed.ac.id |
| Source: | Telkomnika. Jun2026, Vol. 24 Issue 3, p891-903. 13p. |
| Subjects: | Ensemble learning, Feature selection, Shapley Additive Explanations, Arrhythmia, Machine learning |
| Abstract: | Premature cardiac contractions, including premature atrial contractions (PACs) and premature ventricular contractions (PVCs), are common arrhythmias that may increase the risk of cardiovascular complications when they occur frequently. Accurate classification of these events from electrocardiogram (ECG) signals remains challenging due to noise and signal variability. This study proposes a machine learning-based classification framework that combines recursive feature elimination with cross-validation for feature selection and an ensemble learning strategy to improve classification robustness. The approach was evaluated using the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) Arrhythmia database and achieved high classification performance, with an accuracy of 95.34%, F1-score of 92.11%, and balanced precision and recall for PVC and PAC. In addition, SHapley Additive exPlanations (SHAP) were employed to identify the most influential features, enhancing model interpretability. The results demonstrate that the proposed framework provides a reliable and interpretable solution for distinguishing premature cardiac contractions, highlighting its potential application in clinical decision support systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195172776 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Classification of premature cardiac contractions based on RFECV and ensemble learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nurdiniyah%2C+Elsa+Sari+Hayunah%22">Nurdiniyah, Elsa Sari Hayunah</searchLink><relatesTo>1</relatesTo><i> elsa.nurdiniyah@unsoed.ac.id</i><br /><searchLink fieldCode="AR" term="%22Yusuf%2C+A'isya+Nur+Aulia%22">Yusuf, A'isya Nur Aulia</searchLink><relatesTo>1</relatesTo><i> aisya.yusuf@unsoed.ac.id</i><br /><searchLink fieldCode="AR" term="%22Amalia%2C+Norma%22">Amalia, Norma</searchLink><relatesTo>1</relatesTo><i> norma.amalia@unsoed.ac.id</i><br /><searchLink fieldCode="AR" term="%22Purnomo%2C+Widhiatmoko+Herry%22">Purnomo, Widhiatmoko Herry</searchLink><relatesTo>1</relatesTo><i> widhiatmoko.purnomo@unsoed.ac.id</i><br /><searchLink fieldCode="AR" term="%22Hafizha%2C+Azizah+Najda%22">Hafizha, Azizah Najda</searchLink><relatesTo>1</relatesTo><i> azizah.hafizha@mhs.unsoed.ac.id</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Telkomnika%22">Telkomnika</searchLink>. Jun2026, Vol. 24 Issue 3, p891-903. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Shapley+Additive+Explanations%22">Shapley Additive Explanations</searchLink><br /><searchLink fieldCode="DE" term="%22Arrhythmia%22">Arrhythmia</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Premature cardiac contractions, including premature atrial contractions (PACs) and premature ventricular contractions (PVCs), are common arrhythmias that may increase the risk of cardiovascular complications when they occur frequently. Accurate classification of these events from electrocardiogram (ECG) signals remains challenging due to noise and signal variability. This study proposes a machine learning-based classification framework that combines recursive feature elimination with cross-validation for feature selection and an ensemble learning strategy to improve classification robustness. The approach was evaluated using the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) Arrhythmia database and achieved high classification performance, with an accuracy of 95.34%, F1-score of 92.11%, and balanced precision and recall for PVC and PAC. In addition, SHapley Additive exPlanations (SHAP) were employed to identify the most influential features, enhancing model interpretability. The results demonstrate that the proposed framework provides a reliable and interpretable solution for distinguishing premature cardiac contractions, highlighting its potential application in clinical decision support systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.12928/TELKOMNIKA.v24i3.27584 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 891 Subjects: – SubjectFull: Ensemble learning Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Shapley Additive Explanations Type: general – SubjectFull: Arrhythmia Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Classification of premature cardiac contractions based on RFECV and ensemble learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nurdiniyah, Elsa Sari Hayunah – PersonEntity: Name: NameFull: Yusuf, A'isya Nur Aulia – PersonEntity: Name: NameFull: Amalia, Norma – PersonEntity: Name: NameFull: Purnomo, Widhiatmoko Herry – PersonEntity: Name: NameFull: Hafizha, Azizah Najda IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16936930 Numbering: – Type: volume Value: 24 – Type: issue Value: 3 Titles: – TitleFull: Telkomnika Type: main |
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