A Hybridized Data Clustering for Breast Cancer Prognosis and Risk Exposure Using Fuzzy C-means and Cohort Intelligence

  • Meeta Kumar
  • Anand J. KulkarniEmail author
  • Suresh Chandra Satapathy
Part of the Algorithms for Intelligent Systems book series (AIS)


Breast cancer is the most prevailing type of cancer responsible for a large number of deaths every year. However, at the same time, this is largely a curable type of cancer if identified at initial stages. With major advances in research in the areas of image processing, data mining and clustering and machine learning, a more precise prognosis and prediction of breast cancer are possible at earlier stages. A fuzzy clustering model is a popular model used across various researches in image processing to predict the malignancy of breast tumor. The partitional clustering method finds its strength in its fuzzy partitioning such that a data point may belong to different classes with varying degrees of membership (ranging between 0 and 1), which is less rigid as compared to an older and still popular k-means clustering algorithm. The current article attempts to hybridize the fuzzy C-means with the cohort intelligence (CI) algorithm to optimize cluster formation. CI is a robust optimization metaheuristic belonging to the class of socio-inspired optimizers (Kumar M, Kulkarni A Socio-cultural inspired metaheuristics, pp 1–28, Springer International Publishing, 2019 [22]), motivated from self-adapting behavior of candidates in a cohort or a group. CI is typically characterized by its simple algorithmic nature, robust structure and a faster convergence rate, hence gaining popularity. This novel hybridized data clustering algorithm fuzzy-CI imitates the soft clustering and communal learning attitude of clusters and candidates. The hybridized method of fuzzy-CI is validated by testing it on the Breast Cancer Wisconsin (Diagnostic) Dataset. The results validate that the hybridized version exhibits better cluster formation in comparison with the non-hybridized version.


Fuzzy C-means (FCM) Cohort intelligence (CI) Breast cancer detection 


  1. 1.
    Agrawal S, Agrawal J (2015) Neural network techniques for cancer prediction: a survey. Proc Comput Sci 60:769–774CrossRefGoogle Scholar
  2. 2.
    Ahmad LG, Eshlaghy AT, Poorebrahimi A, Ebrahimi M, Razavi AR (2013) Using three machine learning techniques for predicting breast cancer recurrence. J Health Med Inform 4(124):3Google Scholar
  3. 3.
    Asri H, Mousannif H, Al Moatassime H, Noel T (2016) Using machine learning algorithms for breast cancer risk prediction and diagnosis. Proc Comput Sci 83:1064–1069CrossRefGoogle Scholar
  4. 4.
    Asuncion A, Newman DJ (2007) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CA.
  5. 5.
    Ayer T, Alagoz O, Chhatwal J, Shavlik JW, Kahn CE Jr, Burnside ES (2010) Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration. Cancer 116(14):3310–3321CrossRefGoogle Scholar
  6. 6.
    Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203CrossRefGoogle Scholar
  7. 7.
    Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394–424Google Scholar
  8. 8.
    Cebeci Z, Yildiz F (2015) Comparison of K-means and fuzzy C-means algorithms on different cluster structures. Agrárinformatika/J Agric Inform 6(3):13–23Google Scholar
  9. 9.
    Chattopadhyay S, Pratihar DK, Sarkar SCD (2012) A comparative study of fuzzy c-means algorithm and entropy-based fuzzy clustering algorithms. Comput Inform 30(4):701–720zbMATHGoogle Scholar
  10. 10.
    Dubey AK, Gupta U, Jain S (2016) Analysis of k-means clustering approach on the breast cancer Wisconsin dataset. Int J Comput Assist Radiol Surg 11(11):2033–2047CrossRefGoogle Scholar
  11. 11.
    Frigui H, Krishnapuram R (1999) A robust competitive clustering algorithm with applications in computer vision. IEEE Trans Pattern Anal Mach Intell 21(5):450–465CrossRefGoogle Scholar
  12. 12.
    Gayathri BK, Raajan P (2016) A survey of breast cancer detection based on image segmentation techniques. In: International conference on computing technologies and intelligent data engineering (ICCTIDE). IEEE, pp 1–5Google Scholar
  13. 13.
    Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. ElsevierGoogle Scholar
  14. 14.
    Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. J R Stat Soc Ser C (Appl Stat) 28(1):100–108Google Scholar
  15. 15.
    Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323CrossRefGoogle Scholar
  16. 16.
    Kamalakannan J, Krishna PV, Babu MR, Mukeshbhai KD (2015) Identification of abnormality from digital mammogram to detect breast cancer. In: 2015 international conference on circuits, power and computing technologies (ICCPCT-2015). IEEE, pp 1–5Google Scholar
  17. 17.
    Kashyap KL, Bajpai MK, Khanna P (2015) Breast cancer detection in digital mammograms. In: 2015 IEEE international conference on imaging systems and techniques (IST). IEEE pp 1–6Google Scholar
  18. 18.
    Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: a review of classification techniques. Emerg Artif Intell Appl Comput Eng 160:3–24Google Scholar
  19. 19.
    Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8–17CrossRefGoogle Scholar
  20. 20.
    Krishnasamy G, Kulkarni AJ, Paramesran R (2014) A hybrid approach for data clustering based on modified cohort intelligence and K-means. Expert Syst Appl 41(13):6009–6016CrossRefGoogle Scholar
  21. 21.
    Kulkarni AJ, Durugkar IP, Kumar M (2013) Cohort intelligence: a self supervised learning behavior. In: 2013 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 1396–1400Google Scholar
  22. 22.
    Kumar M, Kulkarni A (2019) Socio-inspired optimization metaheuristics: a review. In: Socio-cultural inspired metaheuristics, pp 1–28. Springer International Publishing (In Press)Google Scholar
  23. 23.
    Lafta HA, Ayoob NK (2013) Breast cancer diagnosis using genetic fuzzy rule based system. J Univ Babylon 21(4):1109–1120Google Scholar
  24. 24.
    Leung Y, Zhang JS, Xu ZB (2000) Clustering by scale-space filtering. IEEE Trans Pattern Anal Mach Intell 22(12):1396–1410CrossRefGoogle Scholar
  25. 25.
    Mangasarian OL, Setiono R, Wolberg WH (1990) Pattern recognition via linear programming: theory and application to medical diagnosis. Large-scale Numer Opt 22–31Google Scholar
  26. 26.
    Medjahed SA, Saadi TA, Benyettou A (2013) Breast cancer diagnosis by using k-nearest neighbor with different distances and classification rules. Int J Comput Appl 62(1)Google Scholar
  27. 27.
    Michalski RS, Carbonell JG, Mitchell TM (eds) (2013) Machine learning: an artificial intelligence approach. Springer Science & Business MediaGoogle Scholar
  28. 28.
    Odajima K, Pawlovsky AP (2014) A detailed description of the use of the kNN method for breast cancer diagnosis. In: 2014 7th international conference on biomedical engineering and informatics (BMEI). IEEE, pp 688–692Google Scholar
  29. 29.
    Ojha U, Goel S (2017) A study on prediction of breast cancer recurrence using data mining techniques. In: 2017 7th international conference on cloud computing, data science and engineering-confluence. IEEE, pp 527–530Google Scholar
  30. 30.
    Panda S, Sahu S, Jena P, Chattopadhyay S (2012) Comparing fuzzy-C means and K-means clustering techniques: a comprehensive study. In: Advances in computer science, engineering and applications. Springer, Berlin, Heidelberg, pp 451–460Google Scholar
  31. 31.
    Ramani R, Valarmathy S, Vanitha NS (2013) Breast cancer detection in mammograms based on clustering techniques—a survey. Int J Comput Appl 62(11)Google Scholar
  32. 32.
    Suganya R, Shanthi R (2012) Fuzzy c-means algorithm—a review. Int J Sci Res Publ 2(11):1Google Scholar
  33. 33.
    Suthaharan S (2016) Machine learning models and algorithms for big data classification. Integr Ser Inf Syst 36:1–12MathSciNetzbMATHGoogle Scholar
  34. 34.
    Verma A, Khanna G (2016) A survey on image processing techniques for tumor detection in mammograms. In: 2016 3rd international conference on computing for sustainable global development (INDIACom). IEEE, pp 988–993Google Scholar
  35. 35.
    Yang MS (1993) A survey of fuzzy clustering. Math Comput Model 18(11):1–16MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Meeta Kumar
    • 1
  • Anand J. Kulkarni
    • 1
    • 2
    Email author
  • Suresh Chandra Satapathy
    • 3
  1. 1.Symbiosis Institute of Technology, Symbiosis International UniversityPuneIndia
  2. 2.Odette School of BusinessUniversity of WindsorWindsorCanada
  3. 3.Department of Computer Science and EngineeringPVP Siddhartha Institute of TechnologyVijayawadaIndia

Personalised recommendations