The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Comparison of the Effects of Cross-validation Methods on Determining Performances of Classifiers Used in Diagnosing Congestive Heart Failure
Open accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Isler, in 2015, observed that using k-fold cross validation in diagnosing congestive heart failure affected the results of the study by falsely enhancing them. The leave-one-patient-out cross-validation method was, as observed, the validation method with the best validity [47].”
From Alzheimer's Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods (2021) · cites this paper as [47]
In the citing paper
“So, similar to the studies reported previously [47,48] the k-fold cross-validation method is not the optimal method for classifying epoched datasets of EEG signals at this case.”
From Alzheimer's Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods (2021) · cites this paper as [47]
Citations
Cites
Heart rate variability: Standards of measurement, physiological interpretation, and clinical use
Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., & Schwartz, P. J. (1996) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a014868 · Cited by 15,289
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Physiological time-series analysis using approximate entropy and sample entropy
Richman, J. S., & Moorman, J. R. (2000) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2000.278.6.h2039 · Cited by 7,880
Least-squares frequency analysis of unequally spaced data
Lomb, N. (1976) · Astrophysics and Space Science · doi:10.1007/bf00648343 · Cited by 5,724
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001) · Advanced textbooks in control and signal processing · doi:10.1007/978-1-4471-0347-9_3 · Cited by 5,053
Pattern Classification (2nd Edition)
Duda, R. O., Hart, P. E., & Stork, D. G. (2000) · Wiley-Interscience eBooks · Cited by 2,070
Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability
Brennan, M., Palaniswami, M., & Kamen, P. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.959330 · Cited by 1,044
Huikuri, H. V., Mäkikallio, T. H., Peng, C., Goldberger, A. L., Hintze, U., & Møller, M. (2000) · Circulation · doi:10.1161/01.cir.101.1.47 · Cited by 668
Patterns of beat-to-beat heart rate variability in advanced heart failure
Woo, M. A., Stevenson, W. G., Moser, D. K., Trelease, R. B., & Harper, R. M. (1992) · American Heart Journal · doi:10.1016/0002-8703(92)90510-3 · Cited by 430
Cited by
Sorted by citations
Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA
Kakadiaris, I. A., Vrigkas, M., Yen, A. A., Kuznetsova, T., Budoff, M., & Naghavi, M. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.009476 · Cited by 227
Miltiadous, A., Tzimourta, K. D., Giannakeas, N., Tsipouras, M. G., Afrantou, T., Ioannidis, P., & Tzallas, A. T. (2021) · Diagnostics · doi:10.3390/diagnostics11081437 · Cited by 160
Li, Y., Yan, C. G., Liu, W., & Li, M. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.07.027 · Cited by 139
Performance of small-world feedforward neural networks for the diagnosis of diabetes
Erkaymaz, O., Özer, M., & Perc, M. (2017) · Applied Mathematics and Computation · doi:10.1016/j.amc.2017.05.010 · Cited by 93
Hussain, L., Awan, I. A., Aziz, W., Saeed, S., Ali, A., Zeshan, F., & Kwak, K. S. (2020) · BioMed Research International · doi:10.1155/2020/4281243 · Cited by 86
Metadata
- Authors
- Isler, Y., Narin, A., & Ozer, M.
- Affiliations
- Department of Biomedical Engineering, Izmir Katip Celebi Univertsity, Balatcik Campus, 35620, Izmir, Turkey
- Zonguldak Bülent Ecevit University (TR)
- Department of Electrical and Electronics Engineering, Bulent Ecevit University, Incivez, 67100, Zonguldak, Turkey
- ORCID
- Narin, Ali
- Venue
- Measurement Science Review
- Publisher
- Walter de Gruyter GmbH
- Published
- 2015-08-01
- Volume
- 15
- Issue
- 4
- Pages
- 196-201
- ISSN
- 1335-8871 (ISSN-L)
- OpenAlex
- W2567386918
- Cited by
- 45
- References
- 28
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 1 · 2022: 5 · 2021: 12 · 2020: 4 · 2019: 4 · 2018: 5 · 2017: 3 · 2016: 1 · 2015: 2
- FWCI
- 7.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Measurement Science Review (publishedVersion, cc-by-nc-nd)
- Zonguldak Bülent Ecevit University Institutional Repository (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- congestive heart failure, heart failure diagnosis, classifier evaluation, cross-validation, k-fold cross-validation, leave-one-out cross-validation, early diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Comparison of the Effects of Cross-validation Methods on Determining Performances of Classifiers Used in Diagnosing Congestive Heart Failure. https://app.cochat.ai/papers/10.1515/msr-2015-0027
- The paper
- Isler, Y., Narin, A., & Ozer, M. (2015). Comparison of the Effects of Cross-validation Methods on Determining Performances of Classifiers Used in Diagnosing Congestive Heart Failure. Measurement Science Review. https://doi.org/10.1515/msr-2015-0027
