A Hybrid Feature Selection and Ensemble Stacked Learning Models on Multi-Variant CVD Datasets for Effective Classification-Reference-Cited by-同舟云学术

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A Hybrid Feature Selection and Ensemble Stacked Learning Models on Multi-Variant CVD Datasets for Effective Classification

Published:2024 Issue: Volume:12 Page:87023-87038
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mahajan Abhigya¹^ORCID,Kaushik Baijnath¹^ORCID,Rahmani Mohammad Khalid Imam²^ORCID,Banga Abdulbasid S.²

Affiliation:

1. School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India

2. College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10552740.pdf?arnumber=10552740

Reference67 articles.

1. Primary prevention of cardiovascular disease: A review of contemporary guidance and literature

2. Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries

3. Cardiovascular disease risk factors among undergraduate medical students in a tertiary care centre of eastern India: a pilot study

4. Cardiovascular disease (CVD) and associated risk factors among older adults in six low-and middle-income countries: results from SAGE Wave 1

5. Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

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