Leave-one-out cross-validation yielded the highest average performance and lowest variance for diagnosing congestive heart failure, with the K-Nearest Neighbors classifier achieving 80.9% accuracy.
Increasing the number of data sections in cross-validation (e.g., leave-one-out) enhances average performance and decreases variability when evaluating classifiers for congestive heart failure diagnosis using heart rate variability.
Abstract 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.
İşler et al. (2015) studied Congestive Heart Failure (n=83). Leave-one-out cross-validation vs. k-fold cross-validation was evaluated on Classifier performance (accuracy, sensitivity, specificity). Leave-one-out cross-validation yielded the highest average performance and lowest variance for diagnosing congestive heart failure, with the K-Nearest Neighbors classifier achieving 80.9% accuracy.