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  Evaluation of Machine Learning Methods for the Long-Term Prediction of Cardiac Diseases

Schlemmer, A., Zwirnmann, H., Zabel, M., Parlitz, U., & Luther, S. (2014). Evaluation of Machine Learning Methods for the Long-Term Prediction of Cardiac Diseases. In 8th Conference of the ESGCO (pp. 157-158). IEEE.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-0F7B-2 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-0F7C-F
Genre: Conference Paper

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 Creators:
Schlemmer, Alexander1, Author              
Zwirnmann, Henning1, Author              
Zabel, Markus, Author
Parlitz, Ulrich1, Author              
Luther, Stefan1, Author              
Affiliations:
1Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063288              

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Free keywords: MYOCARDIAL-INFARCTION
 Abstract: We evaluate several machine learning algorithms in the context of long-term prediction of cardiac diseases. Results from applying K Nearest Neighbors Classifiers (KNN), Support Vector Machines (SVM) and Random Forests (RF) to data from a cardiological long-term study suggests that multivariate methods can significantly improve classification results. SVMs were found to yield the best results in Matthews Correlation Coefficient and are most stable with respect to a varying number of features.

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Language(s): eng - English
 Dates: 2014
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: eDoc: 708932
DOI: 10.1109/ESGCO.2014.6847567
 Degree: -

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Title: 8th Conference of the European-Study-Group-on-Cardiovascular-Oscillations (ESGCO)
Place of Event: Trento, Italy
Start-/End Date: 2014-05-25 - 2014-05-28

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Title: 8th Conference of the ESGCO
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 157 - 158 Identifier: ISBN: 978-1-4799-3969-5