Skip to main content

Advertisement

SpringerLink
Log in

Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Early detection of disease has become a crucial problem due to rapid population growth in medical research in recent times. With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. Breast cancer is the second most severe cancer among all of the cancers already unveiled. An automatic disease detection system aids medical staffs in disease diagnosis and offers reliable, effective, and rapid response as well as decreases the risk of death. In this paper, we compare five supervised machine learning techniques named support vector machine (SVM), K-nearest neighbors, random forests, artificial neural networks (ANNs) and logistic regression. The Wisconsin Breast Cancer dataset is obtained from a prominent machine learning database named UCI machine learning database. The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value, false-negative rate, false-positive rate, F1 score, and Matthews Correlation Coefficient. Additionally, these techniques were appraised on precision–recall area under curve and receiver operating characteristic curve. The results reveal that the ANNs obtained the highest accuracy, precision, and F1 score of 98.57%, 97.82%, and 0.9890, respectively, whereas 97.14%, 95.65%, and 0.9777 accuracy, precision, and F1 score are obtained by SVM, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiol Soc N Am. 2018;286(3):800–9.

    Google Scholar 

  2. Breast Cancer: Statistics, Approved by the Cancer.Net Editorial Board, 04/2017. [Online]. Available: http://www.cancer.net/cancer-types/breast-cancer/statistics. Accessed 26 Aug 2018.

  3. Mori M, Akashi-Tanaka S, Suzuki S, Daniels MI, Watanabe C, Hirose M, Nakamura S. Diagnostic accuracy of contrast-enhanced spectral mammography in comparison to conventional full-field digital mammography in a population of women with dense breasts. Springer. 2016;24(1):104–10.

    Google Scholar 

  4. Kurihara H, Shimizu C, Miyakita Y, Yoshida M, Hamada A, Kanayama Y, Tamura K. Molecular imaging using PET for breast cancer. Springer. 2015;23(1):24–32.

    Google Scholar 

  5. Azar AT, El-Said SA. Probabilistic neural network for breast cancer classification. Neural Comput Appl. 2013;23(6):1737–51.

    Article  Google Scholar 

  6. Nagashima T, Suzuki M, Yagata H, Hashimoto H, Shishikura T, Imanaka N, Miyazaki M. Dynamic-enhanced MRI predicts metastatic potential of invasive ductal breast cancer. Springer. 2002;9(3):226–30.

    Google Scholar 

  7. Park CS, Kim SH, Jung NY, Choi JJ, Kang BJ, Jung HS. Interobserver variability of ultrasound elastography and the ultrasound BI-RADS lexicon of breast lesions. Springer. 2013;22(2):153–60.

    Google Scholar 

  8. Ayon SI, Islam MM, Hossain MR. Coronary artery heart disease prediction: a comparative study of computational intelligence techniques. IETE J Res. 2020;. https://doi.org/10.1080/03772063.2020.1713916.

    Article  Google Scholar 

  9. Muhammad LJ, Islam MM, Usman SS, Ayon SI. Predictive data mining models for novel coronavirus (COVID-19) infected patients’ recovery. SN Comput Sci. 2020;1(4):206.

    Article  Google Scholar 

  10. Islam MM, Iqbal H, Haque MR, Hasan MK. Prediction of breast cancer using support vector machine and K-Nearest neighbors. In: Proc. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, 2017, pp. 226–229.

  11. Haque MR, Islam MM, Iqbal H, Reza MS, Hasan MK. Performance evaluation of random forests and artificial neural networks for the classification of liver disorder. In: Proc. International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, 2018, pp. 1–5.

  12. Ayon SI, Islam MM. Diabetes prediction: a deep learning approach. Int J Inf Eng Electron Bus (IJIEEB). 2019;11(2):21–7.

    Google Scholar 

  13. Islam MZ, Islam MM, Asraf A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images, 2020. pp. 1–20.

  14. Hasan MK, Islam MM, Hashem MMA. Mathematical model development to detect breast cancer using multigene genetic programming. In: 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), pp. 574–579, 2016.

  15. Sakri SB, Rashid NBA, Zain ZM. Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access. 2018;6:29637–47.

    Article  Google Scholar 

  16. Juneja K, Rana C. An improved weighted decision tree approach for breast cancer prediction. In: International Journal of Information Technology, 2018.

  17. Yue W, et al. Machine learning with applications in breast cancer diagnosis and prognosis. Designs. 2018;2(2):13.

    Article  Google Scholar 

  18. Banu AB, Subramanian PT. Comparison of Bayes classifiers for breast cancer classification. Asian Pac J Cancer Prev (APJCP). 2018;19(10):2917–20.

    Google Scholar 

  19. Chaurasia V, Pal S, Tiwari B. Prediction of benign and malignant breast cancer using data mining techniques. J Algorithms Comput Technol. 2018;12(2):119–26.

    Article  Google Scholar 

  20. Azar AT, El-Metwally SM. Decision tree classifiers for automated medical diagnosis. Neural Comput Appl. 2012;23(7–8):2387–403.

    Google Scholar 

  21. Senapati MR, Mohanty AK, Dash S, Dash PK. Local linear wavelet neural network for breast cancer recognition. Neural Comput Appl. 2013;22(1):125–31.

    Article  Google Scholar 

  22. Senapati MR, Panda G, Dash PK. Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection. Neural Comput Appl. 2014;24(3–4):745–53.

    Article  Google Scholar 

  23. Hasan MK, Islam MM, Hashem MMA (2016) Mathematical model development to detect breast cancer using multigene genetic programming. In: Proc. 5th International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, 2016, pp. 574–579.

  24. Azar AT, El-Said SA. Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl. 2013;24(5):1163–77.

    Article  Google Scholar 

  25. Ferreira P, Dutra I, Salvini R, Burnside E. Interpretable models to predict Breast Cancer. In: Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, 2016, pp. 1507–1511.

  26. Jhajharia S, Verma S, Kumar R. A cross-platform evaluation of various decision tree algorithms for prognostic analysis of breast cancer data. In: Proc. International Conference on Inventive Computation Technologies (ICICT), Coimbatore, 2016, pp. 1–7.

  27. Islam MM, Rahaman A, Islam MR. Development of smart healthcare monitoring system in IoT environment. SN Comput Sci. 2020;1(3):185.

    Article  Google Scholar 

  28. Rahaman A, Islam M, Islam M, Sadi M, Nooruddin S. Developing IoT based smart health monitoring systems: a review. Rev d’Intell Artif. 2019;33(6):435–40.

    Google Scholar 

  29. Breast Cancer Wisconsin (Original) Data Set, [Online]. https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data. Accessed 25 Aug 2018.

  30. James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. 1st ed. New York: Springer; 2013.

    Book  MATH  Google Scholar 

  31. Guido S, Mller AC. Introduction to machine learning with python. Sebastopol: O’Reilly Media Inc.; 2016.

    Google Scholar 

  32. Dwivedi AK. Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput Appl. 2016;29(10):685–93.

    Article  Google Scholar 

  33. Ratner B. Statistical and machine-learning data mining: techniques for better predictive modeling and analysis of big data. Oxford: Chapman and Hall/CRC; 2017.

    MATH  Google Scholar 

  34. Dong L, Wesseloo J, Potvin Y, Li X. Discrimination of mine seismic events and blasts using the fisher classifier, naive bayesian classifier and logistic regression. Rock Mech Rock Eng. 2015;49(1):183–211.

    Article  Google Scholar 

  35. Hosmer DW Jr, Lemeshow S. Applied logistic regression. New York: Wiley; 2004.

    MATH  Google Scholar 

  36. Schumacher M, Roner R, Vach W. Neural networks and logistic regression: part I. Comput Stat Data Anal. 1996;21(6):661–82.

    Article  MATH  Google Scholar 

  37. Vach W, Roner R, Schumacher M. Neural networks and logistic regression: part II. Comput Stat Data Anal. 1996;21(6):683–701.

    Article  MATH  Google Scholar 

  38. Hajmeer M, Basheer I. Comparison of logistic regression and neural network-based classifiers for bacterial growth. Food Microbiol. 2003;20(1):43–55.

    Article  Google Scholar 

  39. Xu Y, Zhu Q, Wang J. Breast cancer diagnosis based on a kernel orthogonal transform. Neural Comput Appl. 2012;21(8):1865–70.

    Article  Google Scholar 

  40. Latchoumi TP, Parthiban L. Abnormality detection using weighed particle swarm optimization and smooth support vector machine. Biomed Res. 2017;28:4749–51.

    Google Scholar 

  41. Kumar UK, Nikhil MBS, Sumangali K. Prediction of breast cancer using voting classifier technique. In: Proc. IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Chennai, 2017, pp. 108–114.

Download references

Acknowledgements

This research was partially supported by Universiti Malaysia Pahang (UMP) through UMP Flagship Grant (RDU192206).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh

    Md. Milon Islam, Md. Rezwanul Haque & Hasib Iqbal

  2. Faculty of Computing, Universiti Malaysia Pahang, 26300, Gambang, Kuantan, Malaysia

    Md. Munirul Hasan & Muhammad Nomani Kabir

  3. Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794-2424, USA

    Mahmudul Hasan

Authors
  1. Md. Milon Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md. Rezwanul Haque
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hasib Iqbal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Md. Munirul Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mahmudul Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Muhammad Nomani Kabir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md. Milon Islam.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Islam, M.M., Haque, M.R., Iqbal, H. et al. Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques. SN COMPUT. SCI. 1, 290 (2020). https://doi.org/10.1007/s42979-020-00305-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-020-00305-w

Keywords

Search

Navigation