IJE TRANSACTIONS A: Basics Vol. 27, No. 10 (October 2014) 1557-1564   

downloaded Downloaded: 871   viewed Viewed: 3232

R. Khezri, R. Hosseini and M. Mazinani
( Received: January 25, 2014 – Accepted: June 26, 2014 )

Abstract    Soft Computing techniques play an important role for decision in applications with imprecise and uncertain knowledge. The application of soft computing disciplines is rapidly emerging for the diagnosis and prognosis in medical applications. Between various soft computing techniques, fuzzy expert system takes advantage of fuzzy set theory to provide computing with uncertain words. In a fuzzy expert system, knowledge is represented as a set of explicit linguistic rules.Diagnosis of breast cancer suffers from uncertainty and imprecision associated to imprecise input measures and incompleteness of knowledge of experts. However there are several technology-oriented studies reported for breast cancer diagnosis, few studies have been reported for the breast cancer prognosis. This research presents a fuzzy expert system for breast cancer prognosis to further support of the process of breast cancer diagnosis. This approach is capable enough to capture ambiguous and imprecise information prevalent in the characterization of the breast cancer. For this,the paper utilizes aMamdani fuzzy reasoning model, which has high interpretability for interacting with human expertsduringprognosis process and consequently early diagnosisof thediseased. The performance results on real patients' dataset reveal the accuracy of the system with an average 95% which shows the superiority of the system in the prognosis process compared to other related works.


Keywords    Fuzzy expert system,Soft computing, Breast Cancer, Prognosis


چکیده    تکنیک های محاسبات نرم نقش بسزایی برای تصمیم گیری در کاربردهای مختلف توام با عدم دقت وعدم قطعیت ایفا می نمایند. کاربرد اصول محاسبات نرم به سرعت در حال افزایش در زمینه های تشخیص و پیشگیری پزشکی است. بین تکنیکهای مختلف محاسبات نرم، سیستم خبره فازی که از تئوری مجموعه های فازی استفاده می نمایدو محاسبات با استفاده از عبارتهای زبانی انجام می دهد. در یک سیستم خبره فازی، دانش در قالب مجموعه ای از عبارتهای زبانی در شکل قوانین بیان می شود.مساله تشخیص سرطان سینه از عدم قطعیت و عدم دفتی که در ورودیهای اندازه گیری شده نادقیق موجود است رنج می بردو عدم قطعیت در دانش افراد خبره و پزشکان رنج می برد.اگرچه مطالعات مبتنی بر تکنولوژی زیادی جهت تشخیص سرطان سینه ازایه شده است، مطالعات اندکی جهت پیشگیری از سرطان سینه گزارش شده است. این تحقیق یک سیستم خبره فازی برای پیشگیری از سرطان سینه جهت حمایت بیشتر از فرایند تشخیص سرطان و جلوگیری از آن ارایه می نماید.بدین منظور این مقاله از مدل سیستم استدلال فازی ممدانی که قابلیت تفسیر بالایی جهت محاوره با افراد خبره در زمینه پزشکی در طول فرایند پیش بینی جهت تشخیص زودهنگام سرطان برخوردار است استفاده می نماید.بررسی و تحلیل نتایج کارایی سیستم بر روی داده های بیماران واقعی صحت عملکرد سیستم را 95% نشان می دهدکه برتری سیستم ارایه شده جهت پیشگیری را در مقایسه با کارهای مشابه آشکار می نماید.



1.        Latha, K., "Visualization of risk in breast cancer using fuzzy logic in matlab environment", International Journal of Computational Intelligence Techniques,  (2013), 0976-0466.

2.        National center for chronic disease prevention and health promotion, breast cancer division, breast cancer and you fact sheet. Dec (2010).

3.        Chen , Y., Zieve D. Breast cancer., Pubmed Health,. Dec (2010)

4.        Mazinani, M., Dehmeshki, J., Hosseini, R., Ellis, T. and Qanadli, S.D., "Automatic segmentation of soft plaque by modeling the partial volume problem in the coronary artery", in Digital Society, Fourth International Conference on, IEEE., (2010), 274-278.

5.        Hosseini, R., Dehmeshki, J., Barman, S., Mazinani, M., Jouannic, A.-M. and Qanadli, S., "A fuzzy logic system for classification of the lung nodule in digital images in computer aided detection", in Digital Society,. Fourth International Conference on, IEEE., (2010), 255-259.

6.        Hosseini, R., Qanadli, S.D., Barman, S., Mazinani, M., Ellis, T. and Dehmeshki, J., "An automatic approach for learning and tuning gaussian interval type-2 fuzzy membership functions applied to lung cad classification system", Fuzzy Systems, IEEE Transactions on,  Vol. 20, No. 2, (2012), 224-234.

7.        Fatima, B. and Amine, C.M., "A neuro-fuzzy inference model for breast cancer recognition", International Journal of Computer Science & Information Technology,  Vol. 4, No. 5, (2012).

8.        M. Anavari, "A data base model for medical consultation", International Journal of Engineering, Islamic Republic of Iran,  Vol. 4., (1991), 23-29.

9.        Kumar, S., Kumar, V., Abhilasha, A., Garg, M. and Jain, D., "Fourier transform infra red spectroscopic studies on epilepsy, migraine and paralysis", International Journal of Engineering-Transactions B: Applications,  Vol. 23, No. 3&4, (2010), 277.

10.     Mirzaeian, B., Moallem, A. and Lucas, C., "A fuzzy expert system for predicting the performance of switched reluctance motor", International Journal of Engineering,  Vol. 14, No. 3, (2001), 229-238.

11.     Dehghan H. , Pouyan A.A, Hassanpour H. and "Detection of alzheimer’s disease using multitracer positron emission tomography imaging", International Journal of Engineering, Transaction A: Basic,  Vol. 27, No. 1, (2014), 51-56.

12.     Shafiee-Chafi, M. and Gholizade-Narm, H., "A novel fuzzy based method for heart rate variability prediction", International Journal of Engineering-Transactions A: Basics,  Vol. 27, No. 7, (2014), 1041-1054.

13.     Khosravi, A., Addeh, J. and Ganjipour, J., "Breast cancer detection using ba-bp based neural networks and efficient features", in Machine Vision and Image Processing (MVIP), 7th Iranian, IEEE., (2011), 1-6.

14.     Karabatak, M. and Ince, M.C., "An expert system for detection of breast cancer based on association rules and neural network", Expert Systems with Applications,  Vol. 36, No. 2, (2009), 3465-3469.

15.     Mirshra M.K., Abirami T., Soundarya S.R. and Rajasulochana R., "A fuzzy based model for breast cancer diagnosis", International Journal of Scientific and Research Publications,  Vol. 3, No. 3, (2013).

16.     Pena-Reyes, C.A. and Sipper, M., "A fuzzy-genetic approach to breast cancer diagnosis", Artificial intelligence in medicine,  Vol. 17, No. 2, (1999), 131-155.

17.     Hosseini, R., Ellis, T., Mazinani, M. and Dehmeshki, J., "A genetic fuzzy approach for rule extraction for rule-based classification with application to medical diagnosis", in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD).(2011)

18.     Das, A. and Bhattacharya, M., "GA based neuro fuzzy techniques for breast cancer identification", International Machine Vision and Image Processing Conference, IEEE. (2008), 136-141.

19.     Elgader, H.A.A. and Hamza, M.H., "Breast cancer diagnosis using artificial intelligence neural networks"  Journal of Science Technology,(2011) ,159-163.

20.     Hamdan, H. and Garibaldi, J.M., "Adaptive neuro-fuzzy inference system (anfis) in modelling breast cancer survival", in Fuzzy Systems (FUZZ), International Conference on, IEEE, (2010), 1-8.

21.     Balanica V., Dumitrache I., Mihai, C.W.R. and Ch., H., "Evolution of breast cancer  risk by using  fuzzy  logic", U.P.B. Sci. Bull., Series C,  Vol. 73, No. 1, (2011).

22.     Yilmaz, A. and AYAN, K., "Cancer risk analysis by fuzzy logic approach and performance status of the model", Turkish Journal of Electrical Engineering & Computer Sciences,  Vol. 21, No. 3, (2013), 897-912.

23.     Tatari, F., Akbarzadeh-T, M.-R. and Sabahi, A., "Fuzzy-probabilistic multi agent system for breast cancer risk assessment and insurance premium assignment", Journal of Biomedical Informatics,  Vol. 45, No. 6, (2012), 1021-1034.

24.     Caramihai, M., Severin, I., Blidaru, A., Balan, H. and Saptefrati, C., "Evaluation of breast cancer risk by using fuzzy logic", in Proceedings of the 10th WSEAS international conference on applied informatics and communications, and 3rd WSEAS international conference on Biomedical electronics and biomedical informatics., (2010).

25.     Shleeg  A. A. and Ellabib I. M., "Comparison of mamdani and  sugeno fuzzy interference systems for the breast cancer", World Academy of Science  Engineering and Technology ,International Journal of Computer Science and Engineering Vol. 7, No. 10, (2013).

26.     Valarmathi, S., Sulthana A., Rathan R., Latha, K.C., Balasubramanian, S. and Sridhar, R., "Prediction of  risk in breast cancer using fuzzy logic tool box  in  matlab environment", International Journal of Current Research,  Vol. 4, No. 09, (2012), 072-079.

27.     Morai S.S., Duarte  G. M., Torresan R.  and Cabello C., "Breast cancer  prevention: Is it possible to improve the selection by gail model using the fuzzy logic methodology?", Rev. Bras. Biom., Sao Paulo,  Vol. 29, No. 3, (2011), 416-  434.

28.     Zadeh, L.A., "Fuzzy sets", Information and Control,  Vol. 8, No. 3, (1965), 338-353   

International Journal of Engineering
E-mail: office@ije.ir
Web Site: http://www.ije.ir