TY - GEN
T1 - Evaluation of machine learning methods for the long-term prediction of cardiac diseases
AU - Schlemmer, Alexander
AU - Zwirnmann, Henning
AU - Zabel, Markus
AU - Parlitz, Ulrich
AU - Luther, Stefan
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84904469138
U2 - 10.1109/ESGCO.2014.6847567
DO - 10.1109/ESGCO.2014.6847567
M3 - Conference contribution
AN - SCOPUS:84904469138
SN - 9781479939695
T3 - 2014 8th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2014
SP - 157
EP - 158
BT - 2014 8th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2014
PB - IEEE Computer Society
T2 - 2014 8th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2014
Y2 - 25 May 2014 through 28 May 2014
ER -