Unbalanced Dataset Preprocessing Using Hybrid Combination Algorithm for Arrhythmia Detection

Title

Unbalanced Dataset Preprocessing Using Hybrid Combination Algorithm for Arrhythmia Detection

Creator

Cheruvathoor, Lufiya George; Thomas, Jyothi

Description

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.

Source

Lecture Notes in Networks and Systems;Volume;1228 LNNS;pp.99-108

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

ADASYN; Arrhythmia; Hybrid Algorithm; Preprocessing; RUSBoost Classifi?r; Tom?k Links

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-303178936-6;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-031-78937-3_10

https://www.scopus.com/pages/publications/105009908970?origin=resultslist