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Cited by 1,218

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Predicting breast cancer survivability: a comparison of three data mining methods

Delen, Dursun, Walker, Glenn, Kadam, Amit·2005·Artificial Intelligence in Medicine
DOI 10.1016/j.artmed.2004.07.002
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Research works printed but not linked: 25

  1. Jerez-Aragonés, 2003, A combined neural network and decision trees model for prognosis of breast cancer relapse, Artif. Intell. Med., 27, 45, 10.1016/S0933-3657(02)00086-6 · DOI · Paper page
  2. Edwards, 2002, Annual report to the nation on the status of cancer, 1973–1999, featuring implications of age and aging on US cancer burden, Cancer, 94, 2766, 10.1002/cncr.10593 · DOI · Paper page
  3. Ross E. Breast cancer drug may prolong life. Associated Press; 2003 (http://aolsvc.news.aol.com/news/article.ade?Fid=20030924090209990002&mpc=news%2e10%2e7).
  4. Lavrac, 1999, Selected techniques for data mining in medicine, Artif. Intell. Med., 16, 3, 10.1016/S0933-3657(98)00062-1 · DOI · Paper page
  5. Richards, 2001, Data mining for indicators of early mortality in a database of clinical records, Artif. Intell. Med., 22, 215, 10.1016/S0933-3657(00)00110-X · DOI
  6. O’Malley, 2003, Socioeconomic status and breast carcinoma survival in four racial/ethnic groups: a population-based study, Am. Cancer Soc., 1303
  7. Cios, 2002, Uniqueness of medical data mining, Artif. Intell. Med., 26, 1, 10.1016/S0933-3657(02)00049-0 · DOI · Paper page
  8. Ohno-Machado, 2001, Modeling medical prognosis: survival analysis techniques, J. Biomed. Inform., 34, 428, 10.1006/jbin.2002.1038 · DOI
  9. Brenner, 2002, A computer program for period analysis of cancer patient survival, Eur. J. Cancer, 38, 690, 10.1016/S0959-8049(02)00003-5 · DOI
  10. Bundred, 2001, Prognostic and predictive factors in breast cancer, Cancer Treatment Rev., 27, 137, 10.1053/ctrv.2000.0207 · doi:10.1053/ctrv.2000.0207
  11. D’Eredita’, 2001, Prognostic factors in breast cancer: the predictive value of the Nottingham Prognostic Index in patients with a long-term follow-up that were treated in a single institution, Eur. J. Cancer, 37, 591, 10.1016/S0959-8049(00)00435-4 · DOI · Paper page
  12. Rampaul, 2001, Prognostic and predictive factors in primary breast cancer and their role in patient management: the Nottingham breast team, Eur. J. Surg. Oncol., 27, 229, 10.1053/ejso.2001.1114 · doi:10.1053/ejso.2001.1114
  13. Jimenez-Lee, 2003, Breast cancer severity score is an innovative system for prognosis, Am. J. Surg., 186, 404, 10.1016/S0002-9610(03)00283-6 · doi:10.1016/s0002-9610(03)00283-6
  14. Burke, 1995, Comparing the prediction accuracy of artificial neural networks and other statistical models for breast cancer survival, vol. 7, 1063
  15. Burke, 1997, Artificial neural networks improve the accuracy of cancer survival prediction, Cancer, 79, 857, 10.1002/(SICI)1097-0142(19970215)79:4<857::AID-CNCR24>3.0.CO;2-Y · DOI · Paper page
  16. Lundin, 1999, Artificial neural networks applied to survival prediction in breast cancer, Oncology., 57, 281, 10.1159/000012061 · DOI
  17. Pendharkar, 1999, Association, statistical, mathematical and neural approaches for mining breast cancer patterns, Expert Syst. Applic., 17, 223, 10.1016/S0957-4174(99)00036-6 · doi:10.1016/s0957-4174(99)00036-6
  18. Abbass, 2002, An evolutionary artificial neural networks approach for breast cancer diagnosis, Artif. Intell. Med., 25, 265, 10.1016/S0933-3657(02)00028-3 · DOI · Paper page
  19. Abu-Hanna, 2003, Integrating classification trees with local logistic regression in intensive care prognosis, Artif. Intell. Med., 29, 5, 10.1016/S0933-3657(03)00047-2 · DOI
  20. Santos-Garcia, 2004, Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble, Artif. Intell. Med., 30, 61, 10.1016/S0933-3657(03)00059-9 · DOI · Paper page
  21. Hankey, 1999, The surveillance, epidemiology, and end results program: a national resource., Cancer Epidemiol. Biomarkers Prev., 8, 1117
  22. Hornik, 1990, Universal approximation of an unknown mapping and its derivatives using multilayer feedforward network, Neural Netw., 3, 359, 10.1016/0893-6080(90)90005-6 · DOI · Paper page
  23. Berman, 2002, Confidentiality issues for medical data miners, Artif. Intell. Med., 26, 25, 10.1016/S0933-3657(02)00050-7 · DOI
  24. Bostwick, 2001, Prediction of individual patient outcome in cancer, 91, 1643
  25. Shearer, 2000, The CRISP-DM model: the new blueprint for data mining, J. Data Warehousing, 5, 13

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Metadata

Authors
Delen, D., Walker, G., & Kadam, A.
Venue
Artificial Intelligence in Medicine
Publisher
Elsevier BV
Published
2005-06
Volume
34
Issue
2
Pages
113-127
ISSN
0933-3657 (ISSN-L)
DOI
10.1016/j.artmed.2004.07.002
OpenAlex
W1983024255
Cited by
1,218
References
3
FWCI
46.81
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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CoChat. (2026). Citation check: Predicting breast cancer survivability: a comparison of three data mining methods. https://app.cochat.ai/papers/10.1016/j.artmed.2004.07.002
The paper
Delen, D., Walker, G., & Kadam, A. (2005). Predicting breast cancer survivability: a comparison of three data mining methods. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2004.07.002

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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.