Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Cheruvathoor, Lufiya George; Thomas, Jyothi Title A name given to the resource Unbalanced Dataset Preprocessing Using Hybrid Combination Algorithm for Arrhythmia Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1228 LNNS;pp.99-108 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-78937-3_10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-78937-3_10</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105009908970?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009908970?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Cheruvathoor L.G., Department of Computer Science, CHRIST (Deemed To Be) University, Bangalore, India; Thomas J., Department of Computer Science, CHRIST (Deemed To Be) University, Bangalore, India Description An account of the resource Nowadays timely vaticination of cardiovascular conditions with the aid of a computer-backed opinion system minimizes the mortality. Cardiac arrhythmia discovery is one of the most grueling tasks because the variations of electrocardiogram (ECG) signal are veritably slight, which can not be recognized by mortal eyes. The data set under disquisition in this work is taken from the well-known Arrhythmia Dataset, which is codified into different classes. The correct identification of the health condition can lead to an easier, more effective, and less precious reclamation. This proffered study incorporates times of exploration on arrhythmia discovery exercising coincidental technologies. This project addresses the challenge of class imbalance in the analysis of arrhythmia data, utilizing hybrid combination of thr?? approaches like Tomek Links, RUSBoost Classifier, and ADASYN. Here, used the hybrid combination of these three methods.The study commences with data preprocessing, including the loading and preparation of the dataset. Feature extraction and label separation are performed to enable further analysis. Subsequently, resolve th? dataset into groups of testing and training to facilitate robust model valuation. Additionally, this article provides a thorough analysis of new preprocessing model approaches for diagnosing heart disease. By comparing the performance of th?s? methods, this research contributes to th? development of robust and accurate arrhythmia classification models, with potential applications in clinical diagnostics and healthcare decision support systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource ADASYN; Arrhythmia; Hybrid Algorithm; Preprocessing; RUSBoost Classifi?r; Tom?k Links Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303178936-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online