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

Schlemmer, Alexander; Zwirnmann, Henning; Zabel, Markus; Parlitz, Ulrich; Luther, Stefan

doi:10.1109/ESGCO.2014.6847567

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

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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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Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0029-0F7B-2

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.