A survey on predicting breast cancer survivability and its challenges
DOI:
https://doi.org/10.24200/jrset.vol4iss03pp37-42Abstract
Data mining is a powerful technology that can be used in all domains in order to detect hidden patterns from a large volume of data. A huge amount of medical data gives opportunities to health research community to extract new knowledge in different parts of medicine such as diagnosis, prognosis, and treatment by using data mining applications in order to improve the quality of patient care and reduce healthcare costs. Breast cancer is the most common cancer in women worldwide and it is the leading cause of death among women. Data mining can be used as a decision support system to predict survival of new patients. In this study, related works in the field of breast cancer survival prediction are reviewed and by compromising these works challenging issues are presented. Pages:37-42References
N. Lavrac, “Selected techniques for data mining in medicine,” Artif Intell Med, 1999, vol. 16, pp. 3-23.
M. R. Parsaei, M. Salehi, “E-mail spam detection based on part of speech tagging”, 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), Proceeding of the 2015 IEEE, November 2015pp. 1010 – 1013.
M. R. Parsaei, R. Javidan, and M. J. Sobouti, “Optimization of Fuzzy Rules for Online Fraud Detection with the Use of Developed Genetic Algorithm and Fuzzy Operators,” Asian Journal of Information Technology, 2016, vol. 15, no. 11, pp. 1856-1864.
M. R. Parsaei, R. Taheri and R. Javidan, R, “Perusing The Effect of Discretization of Data on Accuracy of Predicting Naïve Bayes Algorithm,” Journal of Current Research in Science, 2016, (1), pp. 457-462.
M. R. Parsaei, S. M. Rostami and R. Javidan, “A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset,” International Journal of Advanced Computer Science & Applications, 2016, vol. 7, no. 6, pp. 20-25.
S. S. Parsa, M. Sourizaei, M. M. Dehshibi, R. E. Shateri, M. R. Parsaei, “Coarse-grained correspondence-based ancient Sasanian coin classification by fusion of local features and sparse representation-based classifier,” Multimedia Tools and Applications, 2016, 1-26, doi:10.1007/s11042-016-3856-6.
G. Richards, V.J. Rayward-Smith, P.H. Sonksen, S. Carey, and C. Weng, “Data mining for indicators of early mortality in a database of clinical records,” Artif Intell Med, 2001, vol. 22, pp. 215-231.
J. Cruz, and D. Wishart, "Applications of machine learning in cancer prediction and prognosis," Cancer informatics, 2007, vol. 2, pp. 59-77.
H. Lotfnezhad Afshar, M. Ahmadi, M. Roudbari, and F. Sadoughi, "Prediction of breast cancer survival through knowledge discovery in databases," Glob J Health Sci, 2015, vol. 7, pp. 392-8.
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” AI Magazine, 1996, vol. 17, pp. 37-54.
N. Esfandiari, M. R. Babavalian, A.-M. E. Moghadam, and V. K. Tabar, "Knowledge discovery in medicine: Current issue and future trend," Expert Systems with Applications, 2014, vol. 41, pp. 4434-4463.
WHO "Cancer". World Health Organization. . Available: http://www.who.int/mediacentre/factsheets/fs297/en/.
Breast cancer Q&A/facts and statistics. http://www.komen.org/bci/bhealth/QA/q_and_a.asp.
J. G.-R. J. Jerez-Aragone´s, G. Ramos-Jimenez, J. Munoz-Perez, E. Alba Conejo, " A combined neural network and decision trees model for prognosis of breast cancer relapse," Artificial Intelligence in Medicine, 2003, vol. 27, pp. 45-63.
D. Delen, G. Walker, and A. Kadam, "Predicting breast cancer survivability: a comparison of three data mining methods," Artif Intell Med, Jun 2005, vol. 34, pp. 113-27.
American Cancer Society "Report sees 7.6 million global 2007 cancer deaths" Reuters.
U. Khan, H. Shin, J.P. Choi, M. Kim,, "wFDT - Weighted Fuzzy Decision Trees for Prognosis of Breast Cancer Survivability," presented at the AusDM, 2008.
Ministry of Health and Medical Education, Center for Disease Management, Cancer Department. National Registration Cancer Cases Reported in 2010. Iran: Center for Disease Management; 2013. p.344-9.
D. K. S. Gupta, and A. Sharma, "Data mining classification techniques applied for breast cancer diagnosis and prognosis," Indian Journal of Computer Science and Engineering, 2001, vol. 2, pp. 188-193.
D. K. S. Gupta, and A. Sharma, "Data mining classification techniques applied for breast cancer diagnosis and prognosis," Indian Journal of Computer Science and Engineering, 2001, vol. 2, pp. 188-193.
M. Movahedi, S. Haghighat, M. Khayamzadeh, A. Moradi, A. Ghanbari-Motlagh, H. Mirzaei, et al., "Survival rate of breast cancer based on geographical variation in iran, a national study," Iran Red Crescent Med J, 2012, vol. 14, pp. 798-804.
A. Bellaachia, and E. Guven, "Predicting Breast Cancer Survivability using Data Mining Techniques," presented at the Ninth Workshop on Mining Scientific and Engineering Datasets in conjunction with the Sixth SIAM International Conference on Data Mining, 2006.
Ch-M. Chao, Y-W. Yu, B-W. Cheng, and Y-L. Kuo, "Construction the Model on the Breast Cancer Survival Analysis Use Support Vector Machine, Logistic Regression and Decision Tree," J Med Syst, 2014, vol.38.
A. Endo, S. Takeo, and H. Tanaka, "Predicting Breast Cancer Survivability: Comparison of Five Data Mining Techniques," Journal of Korean Society of Medical Informatics, 2007, vol. 13, no 2, pp. 177 180.
J. Thongkam, G. Xu, Y. Zhang, and F. Huang, "Toward breast cancer survivability prediction models through improving training space, " Expert Systems with Applications, 2009, vol. 36, pp. 12200-12209.
Y-Q. Liu, Ch. Wang, L. Zhang, "Decision Tree Based Predictive Models for Breast Cancer Survivability on Imbalanced Data," 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009.
K.-J. Wang, B. Makond, K.-H. Chen, and K.-M. Wang, "A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients," Applied Soft Computing, 2014, vol. 20, pp. 15-24.
Nabaei, A., Hamian, M., Parsaei, M. R., Safdari, R., Samad-Soltani, T., Zarrabi, H., & Ghassemi, A. Topologies and performance of intelligent algorithms: a comprehensive review. Artificial Intelligence Review, 2016, 1-25, doi:10.1007/s10462-016-9517-3.
P. J. Garcia-Laencina, P. H. Abreu, M. H. Abreu, and N. Afonoso, "Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values," Comput Biol Med, Apr 2015, vol. 59, pp. 125-33.