The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
An expert system for detection of breast cancer based on association rules and neural network
Open accessAbstract
This paper presents an automatic diagnosis system for detecting breast cancer based on association rules (AR) and neural network (NN). In this study, AR is used for reducing the dimension of breast cancer database and NN is used for intelligent classification. The proposed AR+NN system performance is compared with NN model. The dimension of input feature space is reduced from nine to four by using AR. In test stage, 3-fold cross validation method was applied to the Wisconsin breast cancer database to evaluate the proposed system performances. The correct classification rate of proposed system is 95.6%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR+NN model can be used to obtain fast automatic diagnostic systems for other diseases.
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
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Applied Multivariate Statistical Analysis.
Johnson, A. R., & Wichern, D. W. (1988) · Biometrics · doi:10.2307/2531616 · Cited by 11,353
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,419
An Introduction to Multivariate Statistical Analysis
Muirhead, R. J., & Anderson, T. W. (1986) · Journal of Business & Economic Statistics · doi:10.2307/1391399 · Cited by 9,082
Neural networks: A comprehensive foundation
Tadeusiewicz, R. (1995) · Control Engineering Practice · doi:10.1016/0967-0661(95)90080-2 · Cited by 7,218
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551
An Introduction to Multivariate Statistical Analysis.
Skinner, C. J., & Anderson, T. W. (1985) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2981950 · Cited by 3,245
Applied Multivariate Statistical Analysis
Härdle, W., & Simar, L. (2003) · Springer eBooks · doi:10.1007/978-3-662-05802-2 · Cited by 2,263
Cited by
Could Machine Learning Break the Convection Parameterization Deadlock?
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., & Yacalis, G. (2018) · Geophysical Research Letters · doi:10.1029/2018gl078202 · Cited by 497
Association rule mining to detect factors which contribute to heart disease in males and females
Nahar, J., Imam, T., Tickle, K. S., & Chen, Y.-P. P. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.08.028 · Cited by 394
Smart health: Big data enabled health paradigm within smart cities
Pramanik, M. I., Lau, R. Y. K., Demirkan, H., & Azad, M. A. K. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.06.027 · Cited by 341
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
Bilal, A., Imran, A., Baig, T. I., Liu, X., Abouel Nasr, E., & Long, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-61322-w · Cited by 97
Metadata
- Authors
- Karabatak, M., & Ince, M. C.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2009-03
- Volume
- 36
- Issue
- 2
- Pages
- 3465-3469
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2124611117
- Cited by
- 494
- References
- 27
- FWCI
- 27.50
- Type
- journal article
- Language
- en
- Open access
- green
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: An expert system for detection of breast cancer based on association rules and neural network. https://app.cochat.ai/papers/10.1016/j.eswa.2008.02.064
- The paper
- Karabatak, M., & Ince, M. C. (2009). An expert system for detection of breast cancer based on association rules and neural network. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2008.02.064
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.
