Predicting acute myocardial infarction from haematological markers utilizing machine learning and explainable artificial intelligence-Reference-Cited by-同舟云学术

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Predicting acute myocardial infarction from haematological markers utilizing machine learning and explainable artificial intelligence

Published:2024-03-26 Issue:1 Volume:12 Page:
ISSN:2164-2583
Container-title:Systems Science & Control Engineering
language:en
Short-container-title:Systems Science & Control Engineering

Author:

Bhat Tejas Kadengodlu¹,Chadaga Krishnaraj²,Sampathila Niranjana¹,KS Swathi³,Chadaga Rajagopala⁴,Umakanth Shashikiran⁵,Prabhu Srikanth²

Affiliation:

1. Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India

2. Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India

3. Department of Social Innovation, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India

4. Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India

5. Department of Medicine, Dr. TMA Pai Hospital, Manipal Academy of Higher Education, Manipal, Karnataka, India

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/21642583.2024.2331074

Reference50 articles.

1. Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance

2. An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques

3. To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods

4. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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