Malasowe Bridget Ogheneovo, Okolie S. O., Awodele Oludele, Omotosho O. J.


Fuzzy logic has different approaches for enhancing personal health care delivery. Currently, breast cancer is rated as the second leading cause of death among women. Previous studies using fuzzy logic were directed at reoccurrence/survivability. However, there is need for early identification of the predisposing factors of the disease and its elimination. This study focuses on developing a Mobile-based Fuzzy Expert System (MFES) to predict an individual risk of initial cancer growth.

The predisposing  risk factors of breast cancer were elicited from four domain experts through direct contact; this was used to generate the fuzzy rules. The fuzzy inference approach was employed to formulate the membership functions.Mamdani approach was used for the system design. The system accommodates imprecision, tolerance and uncertainty to achieve tractability, robustness and low cost.  Java expert system shell running on Android operating system was used to achieve the mobile technology aspect. For the purpose of system evaluation, 2500 data were collected from two health care centers in Nigeria using random sampling.

The result indicated that the fact elicited from the experts served as range values for the 12 risk factors for  fuzzification of the input and thus, 36 rules were generated. The rules were used for the system development. The developed MFES recorded 96% accuracy.

It is therefore recommended that MFES be used to detect breast cancer risk levels early enough. The main contribution of this work is to reduce the incidence rate in contrast to the existing methods currently applied in the diagnosis of breast cancer.


Keywords: Soft Computing, Fuzzy Set, Breast Cancer, Risk Factors, Membership Functions




Soft Computing, Fuzzy Set, Breast Cancer, Risk Factors, Membership Functions

Full Text:



Abbod MF, Von Keyserlingk DG, Linkens DA & Mahfouf M (2001). Survey of Utilization of Fuzzy Technology in Medicine and Healthcare, Fuzzy Sets Syst, 120: 331-349.

Aungst TD. Medical applications for pharmacists using mobile devices. Ann Pharmacother. 2013;47(7–8):1088–1095. (PubMed)

Balanica V., Dumitrache I., Mihai, C.W.R. & Ch., H., (2011). "Evolution of breast cancer risk by using fuzzy logic", U.P.B. Sci. Bull., Series C, Vol. 73, No. 155-64.

Bellaachia Abdelghani & Guven Erhan, (2006). "Predicting Breast Cancer Survivability using Data Mining Techniques," Ninth Workshop on Mining Scientific and Engineering Datasets in conjunction with the Sixth SIAM International Conference on Data Mining,

Bilgic T. & Turksen, I.B, (1999). ”Measurement of membership functions: Theoretical and Empirical work.” Chapter 3 in D. Dubois and H. Prade (eds) Handbook of Fuzzy Sets and Systems Vol. 1, Fundamentals of Fuzzy Sets, Kluwer, pp 195-232

Blechner M.D. (2005). Behaviour of Various Machine Learning Models in the Face of Noisy Data, Harvard -MIT Division of Health Sciences and Technology, Final Project.

Caramihai M., I. Severin, A. Blidaru, H. Balan, & C. Saptefrati, 2014. Evaluation of breast cancer risk by using fuzzy logic. New aspects of applied informatics, biomedical electronics & informatics and communications.

Caramihai Mihai, Victor Balanica, Ioan Dumitrache, William Rae & Charles Herbst, (2011). “Evolution of Brest Cancer Risk By Using Fuzzy Logic”, U.P.B. Sci. Bull., Series C, Vol. 73, Issue 1.

Caramihai, M., Severin, I., Blidaru, A., Balan, H. & Saptefrati, C., (2010). "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.,

Cosima Gretton & Matthew Honeyman (2016). The digital revolution: eight technologies that will change health and care.

El-Bagdady A. A., (1997). Fuzzy Inference System (FIS) based decision- making algorithms © 2016, Inc. All rights reserved. 225 Bush Street, Suite 1900, San Francisco, CA 94104

Global Cancer Facts & Figures, 2015. 3rd Edition.

Guadarrama S., Munoz S. & Vaucheret C., (2004). “Fuzzy Prolog: a new approach using soft constraints propagation”, Fuzzy Sets and Systems, Vol. 144, pp. 127–150.

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

Ishibuchi, H., T. Nakashima & T. Morisawa, (1997). “Simple fuzzy rule-based classification systems performed well on commonly used real-world data sets”, Proceedings of the North American Fuzzy Information Processing Society Meeting, pp. 21–24.

Jain R & Abraham A, (2004 Neuro-fuzzy modeling and control). A comparative study of fuzzy classification methods on breast.

Jang, J.S.R. & SUN, C.T. (1995). In The Proceeding of the IEEE, vol. 83, 378–406.

Khosravi A., Addeh J. & Ganjipour J. (2011) “Breast cancer detection using BA-BP Based neural networks and efficient features. IEEE.

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

Lee C. C. (1990). Fuzzy logic in control systems: fuzzy logic controller II. IEEE Transactions on Systems, Man, and Cybernetics. Volume: 20, Issue: 2

Mamdani E. H. & S. Assilian,( 1975) “An experiment in linguistic synthesis with a fuzzy logic controller.”International Journal of Man-Machine Studies , vol. 7, pp. 1–13, 1975.

Mendel J. (1995). Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83(3):345(377),.

Morai S.S., Duarte G. M., Torresan R. & Cabello C., (2011). "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. 3416- 434.

Naaz S., A. Alam, & Ranjit Biswas (2011). “Effect of diferent defuzzification methods in a fuzzy based load balancing application.” IJCSI International Journal of Computer Science Issues, vol. 8, no. 1, pp. 261–267,.

Negnevitsky M. (2001). Artificial Intelligence: A Guide to Intelligent Systems. Addison Wesley/ Pearson,.

National Institute for Health and Care Excellence (NICE). Copyright © 2016 National Institute for Health and Care Excellence. All rights reserved.

Oprea A., Strungaru R.. & Ungureanu G. M., (2007) .“New segmentation techniques for

breast cancer detection based on mammography”. 1st National Symposium on e-Health and Bioengineering, pp. 153-156.

Ozdalga E, Ozdalga A. & Ahuja N. (2012). The smartphone in medicine: a review of current and potential use among physicians and students. J Med Internet Res. (5):e128. [PMC free article] [PubMed]

Phillips M., Cataneo N.R., Ditkoff B.A., Fisher P., Greenberg J., Gunawardena R., Kwon C.S., Tietje O. & Wong C. (2006). Breast Cancer Research and Treatment, Springer

Pons, O., Vila, M., & Kacprzyk, J. (eds.). (2000). Knowledge Management in Fuzzy Databases.Physica-Verlag, Heidelberg

Sipper M. & Reyes C. A. P., (1999) “A fuzzy genetic approach to breast cancer diagnosis”, Artificial Intelligence in Medicine 17, 131–155.

Sizilio Gláucia RMA , Leite Cicília RM , Guerreiro Ana MG & Neto Adrião D Dória, (2012). Fuzzy method for pre-diagnosis of breast cancer from the Fine Needle Aspirate analysis. BioMedical Engineering OnLine. BioMedical Engineering OnLine201211:83.1186/1475-925X-11-83.© SIZILIO et al.; licensee BioMed Central Ltd. 2012

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

Torres A. & Nieto J.J. (2005). “Fuzzy logic in medicine and bioinformatics”, Journal of Biomedicine and Biotechnology, 91908.

Tsai M.T., Tung P.C. & Chen K.Y., (2011) “Experimental evaluations of proportional–integral–derivative type fuzzy controllers with parameter adaptive methods for an active magnetic bearing system”, Expert Systems, Vol. 28, pp. 5–18,.

Turksen I. B., (1991). “Measurement of membership functions and their acquisition.”

Valarmathi S., Harathi P.B., PrashanthiDevi M., Guhan P. & Balasubramanian S. (2008) Geoinformation Technology for Better Health, 141-144.

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

Wallace S, Clark M. & White J. (2012) ‘It’s on my iPhone’: attitudes to the use of mobile computing devices in medical education, a mixed-methods study. BMJ Open. 2:e001099. [PMC free article] [PubMed]

World Health Organisation (WHO) 2012

WHO 2017. (Visited 12 January, 2017)

World cancer research fund international. (Visited 12 January, 2017)

Yager RR, & Filev DP. (1994). Essentials of Fuzzy Modeling and Control. Wiley.

Yilmaz, A & Ayan, K. (2011). Risk analysis in breast cancer disease by using fuzzy logic and effects of stress level on cancer risk. Scientific Research and Essays Vol. 6(24), pp. 5179-5191. Available online at ISSN 1992-2248 ©2011 Academic Journals

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

Zadeh L. (1965). “Fuzzy sets”, Information and Control, Vol. 8, pp. 338–353

Zadeh L (1973) “Outline of a new approach to the analysis of complex and decision process,” in Proceedings of IEEE Transactions on Systems, Man, and Cybernetics, vol. 1, pp. 28–44

Zadeh L (1975) “The concept of a linguistic variable and its application to approximate reasoning-i.” in Proceedings of Information Sciences:Informatics and Computer Science Intelligent Systems Applications, vol. 8, no. 1, 1975, pp. 119–249.

Zadeh L (1976). A fuzzy-algorithmic approach to the definition of complex or imprecise concepts, Internat. J. Man-Machine Stud. 8 249-291.

Zadeh L (1996). From Computing with Numbers RComputing with Words -- From Systems, vol. 2, pp. 103-111,.Manipulation of Measurements to Manipulation of Perceptions, IEEE Transactions.

Zadeh L (1979). Fuzzy sets and information granularity. In Advances in Fuzzy Set Theory and Applications, M. M. Gupta, R. K. Ragade and R. R. Yager editors, 3–18; North-Holland Publishing Co.: Amsterdam

Zadeh L (1986) Outline of a theory of usuality based on fuzzy logic, Reidel, Dordrecht, Fuzzy Sets Theory and Applications, in: A. Jones, A. Kaufmann, H.J. Zimmerman (Eds.), , , 79-97.

Zadeh L (1986). Outline of a computational approach to meaning and knowledge representation based on a concept of a generalized assignment statement, in: M. ThomaA. Wyner (Eds.), Proc. of the Internat. Seminar on Artificial Intelligence and Man-Machine Systems, Springer Heidelberg, , 198 211.

Zadeh L (1994). Fuzzy logic, neural networks and soft computing, Commun. ACM 37 (3) 77-84.

Zadeh L (1996). Fuzzy logic = computing with words, IEEE Trans. on Fuzzy Systems 4 103-111.

Zadeh L (1997). Toward a theory of a fuzzy information granulation in and centrality L theory and its applications .

Zadeh L (1999). From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions. IEEE Trans Circ Syst 45:105–119.

Zadeh L (2001) A new direction in AI: toward a computational theory of perceptions. AI Mag 22(1):73–84.



  • There are currently no refbacks.

Copyright (c) 2018 International Journal of Advanced Research in Computer Science