Abadeh MS, Habibi J, Soroush E. Induction of Fuzzy Classification Systems via Evolutionary ACO- Based Algorithms. International Journal of Simulation, Systems, Science, Technology, 2008; 9:1- 8. DOI:
https://doi.org/10.1109/AMS.2007.53
American Cancer Society. Breast cancer facts& figures 2009-2010. [cited 2006 Feb 11].Available from:
http://www.cancer.org
Amin, M.B. Edge, S. Greene, F. Byrd, D.R. Brookland, R.K.Washington, M.K. Gershenwald, J.E. Compton, C.C. Hess, K.R. Sullivan, D.C. Jessup, J.M. Brierley, J.D. Gaspar, L.E. Schilsky, R.L. Balch, C.M. Winchester, D.P. Asare, E.A. Madera, M. Gress, D.M. Meyer, L.R. (Eds).2018. 8 edition (September 5, 2018) AJCC Cancer Staging Manual. American College of Surgeons, 1032. DOI:
https://doi.org/10.1089/thy.2017.0102
Asadabadi A, Bahrampour A, Haghdoost A. Prediction of Breast Cancer Survival by Logistic Regression and Artificial Neural Network Models. irje. 2014; 10 (3):1-8 DOI:
https://doi.org/10.32996/jmss
Askarpour, Somayeh and Seyed Hamid Ghafouri, 2015, Investigation of Data Mining Algorithms in Breast Cancer Diagnosis, 3rd Electronic Conference on New Research in Science and Technology, Electronically, Alam-e-Mehran Aseman Co. DOI:
https://doi.org/10.5812/semj-120140
Cancer: Interdisciplinary InternationalJournal of the American Cancer Society1997; 80(9):1803-4
Dehghan P, Mogharabi M, Zabbah I, Layeghi K, Maroosi A. Modeling Breast Cancer Using Data Mining Methods. Journal of Health and Biomedical Informatics. 2018; 4 (4):266-278.
Delen, D. Walker, G. & Kadam, A. (2005). Predicting breast cancer survivability: a comparison of three data mining methods. Artificial intelligence in medicine, 34(2), 113-127 DOI:
https://doi.org/10.1016/j.artmed.2004.07.002
Ganji MF, Abadeh MS. Parallel Fuzzy Rule Learning Using an ACO-Based Algorithm for Medical Data Mining. IEEE Fifth International Conference on Bio-Innspired Compting: theories and Applications, 2010: 573-581 DOI:
https://doi.org/10.1109/BICTA.2010.5645189
Hortobagyi GN, de la Garza Salazar J,Pritchard K, Amadori D, Haidinger R, HudisCA, et al. The global breast cancer burden: variations in epidemiology and survival.Clin Breast Cancer. 2005; 6(5):391- 401. DOI:
https://doi.org/10.3816/cbc.2005.n.043
Hosseini, Raheel and Mehdi Mazinani, 2014, A Fuzzy Mamdani Inference System for Diagnosis of Breast Cancer in Intelligent Computer System using Medical Diagnosis, National Conference on Computer Science and Engineering, Mashhad, Khavaran Institute of Higher Education. DOI:
https://doi.org/10.1109/ICCIC.2015.7435670
Hosseini, Raheel and Mehdi Mazinani, 2014, Classification of Uncertainty Sources in Intelligent Medical Image Analysis and Processing Devices, National Conference on Computer Science and Engineering, Mashhad, Khavaran Institute of Higher Education.
Jafari Souk, Alemi and Hamed Shahbazi, 2015, A Review of Fuzzy Inference Algorithms, 4th National Conference on New Ideas in Electrical Engineering, Isfahan, Islamic Azad University, Isfahan (Khorasgan) Dor:
10.30495/ijsee.2022.1969247.1232
Kenarkoohi A, soleimanjahi H, Falahi S, Riahi Madvar H, Meshkat Z. The application of the new intelligent Adaptive Nero Fuzzy Inference System (ANFIS) in prediction of human papilloma virus oncogenicity potency. J Arak Uni Med Sci. 2011; 13 (4):95-105. DOI:
https://doi.org/10.1007/s11356-012-1027-5
Khanna R, Taneja V, Singh SK, Kumar N, Sreenivas V,Puliyel JM. The clinical risk index of babies (CRIB)score in India. Indian J Pediatr. 2002;69:957-60 DOI:
https://doi.org/10.1007/BF02726013
Khosravanian A, Rahmanimanesh M, Keshavarzi P. Designing a Group Decision-Making System Using a Fuzzy Combination of Regression Methods for Prediction of Benign or Malignant Breast Tumors. ijbd. 2017; 10 (3):55-66 Dor:
20.1001.1.17359406.1396.10.3.6.9
Latif A M, Momeny M, Sarram R, Agha Srram M, Pour Ahmadi A, Haj Ebrahimi Z. Using Data Mining and Genetic Algorithm for Diagnosis of Breast Cancer. ijbd. 2016; 9 (1):45-56 Dor:
20.1001.1.17359406.1395.9.1.6.8
Lundin, M. Lundin, J. Burke, H. B. Toikkanen, S. Pylkkänen, L. & Joensuu, H. (1999). Artificial neural networks applied to survival prediction in breast cancer. Oncology, 57(4), 281-286. DOI:
https://doi.org/10.1159/000012061
Mahdavi, Henganga and Hassan Rashidi, 2015, Diagnosis of breast cancer progression using adaptive neuro-fuzzy clustering and data mining methods, 3rd International Conference on Applied Research in Computer Engineering and Information Technology, Tehran, Malek Ashtar University of Technology DOI:
https://doi.org/10.48301/kssa.2022.277156.1426
Nooshin Bigdeli, Hamed Jabbari, Negar Maleki, An Intelligent Hybrid Method for Detection, Demarcation and Classification of Breast Masses Based on The Characteristics of New Tissues Based on Two Images of Mammography, Machine Vision and Image Processing, Accepted, November 16, 2017 Dor:
20.1001.1.23831197.1397.5.2.5.0
Olfatbakhsh A. Haghighat S. Tabari MR. Hashemi E. Sari F. Kaviani A. Patient Satisfaction and Body Image Following Mastectomy, Breast-Conserving Therapy, and Mastectomy With Reconstruction: A Study in Iran”. Arch Breast Cancer, 2018. 5(4):173-182 DOI:
https://doi.org/10.32768/abc.201854173-182
Padmapriya, B. & Velmurugan, T. (2014, December). A survey on breast cancer analysis using data mining techniques. In 2014 IEEE International Conference on Computational Intelligence and Computing Research (pp. 1-4). IEEE DOI:
https://doi.org/10.1109/ICCIC.2014.7238530
Pendharkar, P. C. Rodger, J. A. Yaverbaum, G. J. Herman, N. & Benner, M. (1999). Association, statistical, mathematical and neural approaches for mining breast cancer patterns. Expert Systems with Applications, 17(3), 223-232. DOI:
https://doi.org/10.1016/s0957-4174(99)00036-6
Sadeghnezhad F, NiknamiSh, Ghaffari M, Effect of health education methods on promoting breast self examination (BSE), Journal of Birjand University of MedicalSciences 2009; 15(4): 38-48. DOI:
https://doi.org/10.4103/jehp.jehp_1119_20
Tsang CH, Kwong S, Wang H. Genetic-Fuzzy Rule Mining Approach and Evolution of Feature Selection Techniques for Anomaly Intrusion Detection. Pattern Recognition, 2009; 40: 2373-2391 DOI:
https://doi.org/10.1016/j.patcog.2006.12.009
Yi, W. & Fuyong, W. (2006, August). Breast cancer diagnosis via supp ort vector machines. In Control Conference, 2006. CCC 2006. Chinese (pp. 1853-1856). IEEE. DOI:
https://doi.org/10.1021/ci0256438
Send comment about this article