Detecting congestive heart failure by extracting multimodal features with synthetic minority oversampling technique (SMOTE) for imbalanced data using robust machine learning techniques-Reference-Cited by-同舟云学术

Detecting congestive heart failure by extracting multimodal features with synthetic minority oversampling technique (SMOTE) for imbalanced data using robust machine learning techniques

Published:2020-08-24 Issue: Volume: Page:1-24
ISSN:1745-5030
Container-title:Waves in Random and Complex Media
language:en
Short-container-title:Waves in Random and Complex Media

Author:

Hussain Lal¹²^ORCID,Lone Kashif Javed²,Awan Imtiaz Ahmed²,Abbasi Adeel Ahmed²,Pirzada Jawad-ur-Rehman²

Affiliation:

1. Department of Computer Science & Information Technology, Neelum Campus, University of Azad Jammu & Kashmir, Muzaffarabad, Pakistan

2. Department of Computer Science & Information Technology, Chattar Kalas Campus, University of Azad Jammu & Kashmir, Muzaffarabad, Pakistan

Publisher

Informa UK Limited

Subject

General Physics and Astronomy,General Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/17455030.2020.1810364

Reference95 articles.

1. APPLICATION OF EMPIRICAL MODE DECOMPOSITION–BASED FEATURES FOR ANALYSIS OF NORMAL AND CAD HEART RATE SIGNALS

2. Power Spectral Analysis of Cardiovascular Variability in Patients at Risk for Sudden Cardiac Death

3. Increased heart rate and reduced heart-rate variability are associated with subclinical inflammation in middle-aged and elderly subjects with no apparent heart disease

4. Clinical and demographic determinants of heart rate variability in patients post myocardial infarction: Insights from the cardiac arrhythmia suppression trial (CAST)

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