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

MPG-Autoren
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Schlemmer,  Alexander
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Zwirnmann,  Henning
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Parlitz,  Ulrich
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Luther,  Stefan
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Zitation

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.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0029-0F7B-2
Zusammenfassung
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.