Breast Cancer Decisive Parameters for Iraqi Women via Data Mining Techniques

  • Suhad Faisal Behadili Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq.
  • Mustafa S. Abd Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq.
  • Iyden Kamil Mohammed Biomedical Department, Al-Khawarzmy Engineering College, University of Baghdad, Baghdad, Iraq.
  • Maha Mohammed Al-SAYYID Oncology Teaching Hospital, Medical City, Baghdad, Iraq.


Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes.
Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value.
Results The evaluation is performed using a training data rather than cross validation. The decision tree algorithm J48 is applied to detect and generate the pattern of attributes, which have the real effect on the class value. Furthermore, the experiments are performed with three machine learning algorithms J48 decision tree, simple logistic, and multilayer perceptron using 10-folds cross validation as a test option, and the percentage of correctly classified instances as a measure to determine the best one from them. As well as, this investigation used the iteration control to check the accuracy gained from the three mentioned above algorithms. Hence, it explores whether the error ratio is decreasing after several iterations of algorithm execution or not.
Conclusion It is noticed that the error ratio of classified instances are decreasing after 5-10 iterations, exactly in the case of multilayer perceptron algorithm rather than simple logistic, and decision tree algorithms. This study realized that the TPS_pre is the most common effective attribute among three main classes of examined dataset. This attribute highly indicates the BC inflammation.
share this Article by


1. Khalid Mahdi Salih, Ayden K Mohemmed, Mahera A1-Shaikh, Maha M A1-Sayyid, Salivary fraction of CA 15-3 and CEA as tumor markers for breast cancer in Iraqi women, International Journal of Engineering and Technology, July 2015, ISSN 2319-8885 Vol.04, Issue.27, Pages:5114-5117.
2. Javaid Iqbal, Ophira Ginsburg, Paula A. Rochon, Ping Sun, Steven A. Narod, Differences in breast cancer stage at diagnosis and cancer-specific survival by race and ethnicity in the United States, MD, FRCPC, JAMA. 2015; 313(2):165-173. doi:10.1001/jama.2014.17322.
3. Madkhali N. A., Santin O., Noble H., Reid, J., Understanding breast health awareness in an Arabic culture: qualitative study protocol, (2016). Journal of Advanced Nursing, 72(9), 2226-2237. DOI: 10.1111/ jan.12979.
4. Menna A. Fouda, Fawzy Z. Sherif, Amr A. Ghannam, Safinaz H. Al-shorbagy, Prognostic value of breast cancer subtypes based On ER/ PR, Her2 expression and Ki-67 index in women received adjuvant therapy after conservative surgery for early stages breast cancer a retrospective clinical study, JSM Clinical Oncology and Research, July 31, 2017.
5. Hanna Fredholm, Breast cancer in young women - aspects on mortality and local recurrence, Ph.D., University Hospital Solna, April 7, 2017.
6. Grace Lynn Estanislao, Ronald Augustine Campos, Breast cancer, primary peritoneal malignant mixed mullerian tumor and fallopian tube carcinoma: incidental concomitant malignancies or evidence for a new genetic cancer predisposition syndrome?, Conference 2018 of the European Society of Gynaecological Oncology Lyon, France October 4-6, 2018.
7. B. M. Gayathri, C. P. Sumathi , T. Santhanam, Breast cancer diagnosis using machine learning algorithms–A Survey, International Journal of Distributed and Parallel Systems (IJDPS) Vol.4, No.3, May 2013.
8. S. Yuvarani, Dr. C. Jothi Venkateswaran, Breast cancer detection in data mining: A Review, Journal of Computer Science and Applications. ISSN 2231-1270 Volume 7, Number 1 (2015), pp. 45-48.
9. P. Saranya, B. Satheeskumar, A Survey on feature selection of cancer disease using data mining techniques, IJCSMC, Vol. 5, Issue 5, May 2016, pg.713 – 719.
10. Anupama Y.K, Amutha .S, Ramesh Babu.D.R, Breast cancer prediction using data mining techniques, IJARSE, Volume No.07, Issue No.01, January 2018.
11. Anupama Y.K, Amutha .S, Ramesh Babu.D.R, Survey on data mining techniques for diagnosis and prognosis of breast cancer, International Journal on Recent and Innovation Trends in Computing and Communication, Volume: 5 Issue: 2, Feb. 2017.
12. Vivek Barot, Prof. Niku Brahmbhatt, A Survey on breast cancer diagnosis using data mining technique, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6, Issue 1, January 2017.
13. Hamid Karim Khani Zand, A Comparative survey on data mining techniques for breast cancer diagnosis and prediction, Indian Journal of Fundamental and Applied Life Sciences, 2015 Vol.5 (S1), pp. 4330-4339/Karim.
14. Nagesh Shukla, Markus Hagenbuchner, Khin Than Win, Jack Yang, Breast cancer data analysis for survivability studies and prediction, Computer Methods and Programs in Biomedicine 155 (2018) 199–208.
15. Pramod Khargonekar, Anthony Sinskey,Charles Miller, Balu Ranganathan, Convergence revolution – piloting the third scientific revolution through start-ups for breast cancer cure, Symbiosis Group, Cancer Science and Research: Open Access, May 10, 2017.
16. Janan Majeed Al-Akeedi, Aqeel Shakir Mahmod, Maha Abed Ali, Potential prognostic roles for Il-6 and Crp in Iraqi women with breast cancer, journal I.J.A.B.R, VOL. 3(4) 2013: 530-534.
17. E. A Rakha, D. Soria, A. R. Green, C. Lemetre, D. G. Powe, C. C. Nolan, J. M. Garibaldi, G. Ball, I. O. Ellis, Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer, British Journal of Cancer 110, 1688–1697 (01 April 2014).
18. A.E. Hassanian, Rough set approach for attribute reduction and rule generation: a case of patients with suspected breast cancer, J. Am. Soc. Inform. Sci Technol.2004.
19. Shweta Kharya, Using data mining techniques for diagnosis and prognosis of cancer disease, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012.
20. Dursun Delen, Glenn Walker, Amit Kadam, Predicting breast cancer survivability: a comparison of three data mining methods, Artificial Intelligence in Medicine-2004.
21. Shiv Shakti Shrivastava, Anjali Sant, Ramesh Prasad Aharwal, An overview on data mining approach on breast cancer data, International Journal of Advanced Computer Research, Volume-3 Number-4 Issue-13 December-2013.
22. G. Ravi Kumar, Dr. G. A. Ramachandra, K. Nagamani, An efficient prediction of breast cancer data using data mining techniques, International Journal of Innovations in Engineering and Technology (IJIET), Vol. 2 Issue 4 August 2013.
23. Vikas Chaurasia, Saurabh Pal, Data mining techniques: To predict and resolve breast cancer survivability, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.1, January-2014, pg. 10-22.
24. Soumadip Ghosh, Sujoy Mondal, Bhaskar Ghosh, A comparative study of breast cancer detection based on SVM and MLP BPN classifier, IEEE 2014 First International Conference on Automation, Control, Energy and Systems, India.
25. A. Priyanga, S.Prakasam, Effectiveness of data mining - based cancer prediction system (DMBCPS), International Journal of Computer Applications (0975 – 8887), Volume 83 – No 10, December 2013.
26. R. Delshi Howsalya, M. Indra, Outlier detection algorithm combined with decision tree classifier for early diagnosis of breast cancer, Int J Adv Engg Tech/Vol. VII/Issue II/April-June, 2016/93-98.
27. Divya Tomar , Sonali Agarwal, A survey on data mining approaches for healthcare, International Journal of Bio-Science and Bio-Technology, Vol.5, No.5 (2013), pp. 241-266, October 2013, DOI: 10.14257/ ijbsbt. 2013.5.5.25.
28. Delen D, Walker G, Kadam A, Predicting breast cancer survivability: a comparison of three data mining methods. Artificial Intelligence in Medicine 34: 113-127, 2005.
29. Walaa Gad, SVM-Kmeans: Support vector machine based on Kmeans clustering for breast cancer diagnosis, International Journal of Computer and Information Technology (ISSN: 2279 – 0764) Volume 05 – Issue 02, March 2016, 252-257.
30. Ilias Maglogiannis, Elias Zafiropoulos, An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers, Ioannis Anagnostopoulos, 12 July 2007.
31. Tareef Kamil Mustafa, Mustafa S. Abd, Proposed Approach for Analysing General Hygiene Information Using Various Data Mining Algorithms, Iraqi Journal of Science 58(1B):337-344, January 2017.
How to Cite
BEHADILI, Suhad Faisal et al. Breast Cancer Decisive Parameters for Iraqi Women via Data Mining Techniques. Journal of Contemporary Medical Sciences, [S.l.], v. 5, n. 2, apr. 2019. ISSN 2413-0516. Available at: <>. Date accessed: 16 june 2019.