The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Breast cancer diagnosis using least square support vector machine
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., & Forman, D. (2011) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.20107 · Cited by 54,679
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Global Cancer Statistics, 2002
Parkin, D. M., Bray, F., Ferlay, J., & Pisani, P. (2005) · CA: A Cancer Journal for Clinicians · doi:10.3322/canjclin.55.2.74 · Cited by 18,196
Least Squares Support Vector Machine Classifiers
Suykens, J. A. K., & Vandewalle, J. (1999) · Neural Processing Letters · doi:10.1023/a:1018628609742 · Cited by 9,362
The Nature of Statistical Learning Theory
Sain, S. R. (1996) · Technometrics · doi:10.1080/00401706.1996.10484565 · Cited by 5,434
Improved Use of Continuous Attributes in C4.5
Quinlan, J. R. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.279 · Cited by 1,846
Obtaining interpretable fuzzy classification rules from medical data
Nauck, D., & Kruse, R. (1999) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(98)00070-0 · Cited by 394
Guest Editors' Introduction: On Applied Research in Machine Learning
Provost, F., & Kohavi, R. (1998) · Machine Learning · doi:10.1023/a:1007442505281 · Cited by 378
A fuzzy-genetic approach to breast cancer diagnosis
Peña-Reyes, C. A., & Sipper, M. (1999) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(99)00019-6 · Cited by 367
Generating concise and accurate classification rules for breast cancer diagnosis
Setiono, R. (2000) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(99)00041-x · Cited by 250
Cited by
Machine learning in medical applications: a review of state-of-the-art methods
Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105458 · Cited by 542
Zheng, B., Yoon, S. W., & Lam, S. S. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.08.044 · Cited by 549
Evaluation of artificial intelligence techniques in disease diagnosis and prediction
Ghaffar Nia, N., Kaplanoglu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 426
Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
Houssein, E. H., Emam, M. M., Ali, A. A., & Suganthan, P. N. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114161 · Cited by 404
Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic
Nguyen, C., Wang, Y., & Nguyen, H. N. (2013) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2013.65070 · Cited by 310
Metadata
- Authors
- Polat, K., & Güneş, S.
- Venue
- Digital Signal Processing
- Publisher
- Elsevier BV
- Published
- 2007-07
- Volume
- 17
- Issue
- 4
- Pages
- 694-701
- ISSN
- 1051-2004 (ISSN-L)
- OpenAlex
- W2056137745
- Cited by
- 386
- References
- 13
- FWCI
- 6.45
- Type
- journal article
- Language
- en
- Open access
- closed
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: Breast cancer diagnosis using least square support vector machine. https://app.cochat.ai/papers/10.1016/j.dsp.2006.10.008
- The paper
- Polat, K., & Güneş, S. (2007). Breast cancer diagnosis using least square support vector machine. Digital Signal Processing. https://doi.org/10.1016/j.dsp.2006.10.008
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
