Machine Learning Models to Predict Myocardial Infarction Within 10-Years Follow-up of Cardiovascular Disease Progression-Reference-Cited by-同舟云学术

Machine Learning Models to Predict Myocardial Infarction Within 10-Years Follow-up of Cardiovascular Disease Progression

Published:2022-09-27 Issue: Volume: Page:
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Container-title:2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
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Author:

Tsarapatsani Konstantina-Helen¹,Sakellarios Antonis²,Pezoulas Vasileios C.²,Tsakanikas Vasilis D.²,Matsopoulos George K.³,Marz Winfried⁴,Kleber Marcus⁴,Fotiadis Dimitrios I.²

Affiliation:

1. Unit of Medical Technology and Intelligent Systems, Research Institute-FORTH and School of Electrical and Computer Engineering, National Technical University of Athens,Dept. of Materials Science and Engineering and Biomedical,Ioannina,Greece

2. Unit of Medical Technology and Intelligent Systems, Research Institute-FORTH University of Ioannina,Dept. of Materials Science and Engineering and Biomedical,Ioannina,Greece

3. School of Electrical and Computer Engineering, National Technical University of Athens,Athens,Greece

4. University of Heidelberg,Medical Faculty Mannheim,Dept. of Medicine,Mannheim,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9926747/9926739/09926803.pdf?arnumber=9926803

Reference17 articles.

1. Rationale and design of the LURIC study - a resource for functional genomics, pharmacogenomics and long-term prognosis of cardiovascular disease

2. Advanced methods for missing values imputation based on similarity learning

3. Downsampling for Binary Classification with a Highly Imbalanced Dataset Using Active Learning

4. An improved random forest classifier for multi-class classification

5. XGBoost

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