Comparison of the Effects of Cross-validation Methods on Determining Performances of Classifiers Used in Diagnosing Congestive Heart Failure
Publié en ligne: 27 août 2015
Pages: 196 - 201
Reçu: 18 août 2014
Accepté: 05 août 2015
DOI: https://doi.org/10.1515/msr-2015-0027
Mots clés
© by Yalcin Isler
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Congestive heart failure (CHF) occurs when the heart is unable to provide sufficient pump action to maintain blood flow to meet the needs of the body. Early diagnosis is important since the mortality rate of the patients with CHF is very high. There are different validation methods to measure performances of classifier algorithms designed for this purpose. In this study, k-fold and leave-one-out cross-validation methods were tested for performance measures of five distinct classifiers in the diagnosis of the patients with CHF. Each algorithm was run 100 times and the average and the standard deviation of classifier performances were recorded. As a result, it was observed that average performance was enhanced and the variability of performances was decreased when the number of data sections used in the cross-validation method was increased.