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.
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“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]
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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
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