Revolutionizing Arrhythmia Classification: Unleashing the Power of Machine Learning and Data Amplification for Precision Healthcare
subject: Arrhythmia Classification; Diagnostic Accuracy; Electrocardiogram (ECG) Signals; Healthcare Analytics; Machine Learning Models; Principal Component Analysis (PCA); Random Oversampling
description: This paper presents a comprehensive exploration of arrhythmia classification using machine learning techniques applied to electrocardiogram (ECG) signals. The study delves into the development and evaluation of diverse models, including K-Nearest Neighbors, Logistic Regression, Decision Tree Classifier, Linear and Kernelized Support Vector Machines, and Random Forest. The models undergo rigorous analysis, emphasizing precision and recall due to the categorical nature of the dependent variable. To enhance model robustness and address class imbalances, Principal Component Analysis (PCA) and Random Oversampling are employed. The results highlight the effectiveness of the Kernelized SVM with PCA, achieving a remarkable accuracy of 99.52%. Additionally, the paper discusses the positive impact of feature reduction and oversampling on model performance. The study concludes with insights into the significance of PCA and Random Oversampling in refining arrhythmia classification models, offering potential avenues for future research in healthcare analytics. 2024 IEEE.
creator: Arpan; Singh M.; Garg P.; Srivastava S.; Saggu A.K.
source: Proceedings - 2024 6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024, pp. 516-522.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835037462-9
format: Online
language: English
type: Conference paper
coverage: Arpan, School of Sciences, Christ Deemed to be university, Delhi NCR, India; Singh M., School of Sciences, Christ Deemed to be university, Delhi NCR, India; Garg P., SAITM, Delhi NCR, Gurgoan, India; Srivastava S., School of Sciences, Christ Deemed to be university, Delhi NCR, India; Saggu A.K., School of Sciences, Christ Deemed to be university, Delhi NCR, India