Skip to main content
SpringerLink
Log in

BC-Net: Early Diagnostics of Breast Cancer Using Nested Ensemble Technique of Machine Learning

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

Breast cancer is a divergent and prominent cancer that is responsible for the morbidity and mortality of women throughout the world. This paper aims at early detection and accurate diagnosis of this fatal disease, which is one of the most important steps in breast cancer treatment. Therefore, various nested ensemble machine learning techniques are used to help doctors determine breast cancer at an early stage. The two-layer nested ensemble model has been proposed, which encompasses stacking and voting techniques to detect benign and malignant breast cancer tumors. A total of four two-layer nested ensemble models have been proposed. S(NaiveBayes)-V(3-Meta_Learner), S(BayesNet)-V(3-Meta_Learner), S(NaiveBayes)-V(4-Meta_Learner), and S(BayesNet)-V(4-Meta_Learner) have been designed to contain base learners and meta learners. The experiments have been conducted with the k-fold cross-validation technique for model evaluation. The proposed model is capable of classifying benign and malignant breast cancer tumors with 99.50% accuracy. The aforementioned four models have been compared with previous works in terms of classification accuracy, ROC, recall, precision, TP rate, FP rate, and F1 measure. The experiments showed that the proposed two-layer nested ensemble model S(BayesNet)-V(4-Meta_Learner) performed better than the other three models mentioned supra and competed with all the previously published works. This would help the scientific community and health practitioners diagnose breast cancer with early and accurate results.

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. Dora, L., Agrawal, S., Panda, R., and Abraham, A., Optimal breast cancer classification using Gauss–Newton representation based algorithm, Expert Syst. Appl., 2017, vol. 85, pp. 134–145. https://doi.org/10.1016/j.eswa.2017.05.035

    Article  Google Scholar 

  2. Zheng, B., Yoon, S.W., and Lam, S.S., Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms, Expert Syst. Appl., 2014, vol. 41, no. 4, pp. 1476–1482. https://doi.org/10.1016/j.eswa.2013.08.044

    Article  Google Scholar 

  3. Chen, Ch.-H., A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection, Appl. Soft Comput., 2014, vol. 20, pp. 4–14. https://doi.org/10.1016/j.asoc.2013.10.024

    Article  Google Scholar 

  4. Muto, T., Bussey, H.J.R., and Morson, B.C., The evolution of cancer of the colon and rectum, Cancer, 1975, vol. 36, no. 6, pp. 2251–2270. https://doi.org/10.1002/cncr.2820360944

    Article  Google Scholar 

  5. Bhardwaj, A. and Tiwari, A., Breast cancer diagnosis using Genetically Optimized Neural Network model, Expert Syst. Appl., 2015, vol. 42, no. 10, pp. 4611–4620. https://doi.org/10.1016/j.eswa.2015.01.065

    Article  Google Scholar 

  6. Okagbue, H.I., Adamu, P.E., Oguntunde, P.E., Obasi, E.C.M., and Odetunmibi, O.A., Machine learning prediction of breast cancer survival using age, sex, length of stay, mode of diagnosis and location of cancer, Health Technol., 2021, vol. 11, no. 4, pp. 887–893. https://doi.org/10.1007/s12553-021-00572-4

    Article  Google Scholar 

  7. Zhou, Ch.-M., Xue, Q., Wang, Yi., Tong, J., Ji, M., and Yang, J.-J., Machine learning to predict the cancer-specific mortality of patients with primary non-metastatic invasive breast cancer, Surg. Today, 2021, vol. 51, no. 5, pp. 756–763. https://doi.org/10.1007/s00595-020-02170-9

    Article  Google Scholar 

  8. Globocan IAC, Globocan 2018: India factsheet—India Against Cancer, ICMR, 2022. http://cancerindia. org.in/globocan-2018-india-factsheet/. Cited July 16, 2022.

  9. Christoyianni, I., Dermatas, E., and Kokkinakis, G., Fast detection of masses in computer-aided mammography, IEEE Signal Process. Mag., 2000, vol. 17, no. 1, pp. 54–64. https://doi.org/10.1109/79.814646

    Article  Google Scholar 

  10. Elmore, J.G., Wells, C.K., Lee, C.H., Howard, D.H., and Feinstein, A.R., Variability in radiologists' interpretations of mammograms, New Engl. J. Med., 1994, vol. 331, no. 22, pp. 1493–1499. https://doi.org/10.1056/nejm199412013312206

    Article  Google Scholar 

  11. Acharya, U.R., Ng, W.L., Rahmat, K., Sudarshan, V.K., Koh, J.E.W., Tan, J.H., Hagiwara, Yu., Yeong, Ch.H., and Ng, K.H., Data mining framework for breast lesion classification in shear wave ultrasound: A hybrid feature paradigm, Biomed. Signal Process. Control, 2017, vol. 33, pp. 400–410. https://doi.org/10.1016/j.bspc.2016.11.004

    Article  Google Scholar 

  12. Kong, H., Lai, Z., Wang, X., and Liu, F., Breast cancer discriminant feature analysis for diagnosis via jointly sparse learning, Neurocomputing, 2016, vol. 177, pp. 198–205. https://doi.org/10.1016/j.neucom.2015.11.033

    Article  Google Scholar 

  13. Örkcü, H.H. and Bal, H., Comparing performances of backpropagation and genetic algorithms in the data classification, Expert Syst. Appl., 2011, vol. 38, no. 4, pp. 3703–3709. https://doi.org/10.1016/j.eswa.2010.09.028

    Article  Google Scholar 

  14. Breast cancer facts and statistics, Breast Cancer Care, 2022. https://www.breastcancer.org/facts-statistics. Cited July 19, 2022.

  15. Goodman, D.E., Boggess, L.C., and Watkins, A.B., Artificial immune system classification of multiple-class problems, Proc. Intell. Eng. Syst., 2022, vol. 12, pp. 179–184. https://www.semanticscholar.org/paper/artificial-immune-system-classification-of-multiple-Goodman-Boggess/4b0a63a93b6b1b9c19c83c31f163569320352b3d.

    Google Scholar 

  16. Cevikalp, H., Triggs, B., Yavuz, H.S., Küçük, Ya., Küçük, M., and Barkana, A., Large margin classifiers based on affine hulls, Neurocomputing, 2010, vol. 73, nos. 16–18, pp. 3160–3168. https://doi.org/10.1016/j.neucom.2010.06.018

    Article  Google Scholar 

  17. Li, D.-Ch. and Liu, Ch.-W., A class possibility based kernel to increase classification accuracy for small data sets using support vector machines, Expert Syst. Appl., 2010, vol. 37, no. 4, pp. 3104–3110. https://doi.org/10.1016/j.eswa.2009.09.019

    Article  Google Scholar 

  18. Stoean, R. and Stoean, C., Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection, Expert Syst. Appl., 2013, vol. 40, no. 7, pp. 2677–2686. https://doi.org/10.1016/j.eswa.2012.11.007

    Article  MathSciNet  Google Scholar 

  19. Koloseni, D. and Luukka, P., Differential evolution based nearest prototype classifier with optimized distance measures and GOWA, Intelligent Systems’2014, Angelov, P. et al., Eds., Advances in Intelligent Systems and Computing, vol. 322, Cham: Springer, 2014, pp. 753–763. https://doi.org/10.1007/978-3-319-11313-5_66

  20. Chen, H.L., Yang, B., Wang, S., Wang, G., Liu, D.Yo., Li, H.Zh., and Liu, W.B., Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy, Appl. Math. Comput., 2014, vol. 239, pp. 180–197. https://doi.org/10.1016/j.amc.2014.04.039

    Article  MathSciNet  Google Scholar 

  21. Wang, H., Zheng, B., Yoon, S.W., and Ko, H.S., A support vector machine-based ensemble algorithm for breast cancer diagnosis, Eur. J. Oper. Res., 2018, vol. 267, no. 2, pp. 687–699. https://doi.org/10.1016/j.ejor.2017.12.001

    Article  MathSciNet  MATH  Google Scholar 

  22. Wang, D., Wan, S., and Guizani, N., Context-based probability neural network classifiers realized by genetic optimization for medical decision making, Multimedia Tools Appl., 2018, vol. 77, no. 17, pp. 21995–22006. https://doi.org/10.1007/s11042-018-5631-3

    Article  Google Scholar 

  23. Huang, M.-W., Chen, Ch.-W., Lin, W.-Ch., Ke, Sh.-W., and Tsai, Ch.-F., SVM and SVM ensembles in breast cancer prediction, PLoS ONE, 2017, vol. 12, no. 1, p. e0161501. https://doi.org/10.1371/journal.pone.0161501

    Article  Google Scholar 

  24. Rosales-Pérez, A., Escalante, H.J., Gonzalez, J.A., Reyes-Garcia, C., and Coello Coello, C.A., Bias and variance multi-objective optimization for support vector machines model selection, Pattern Recognition and Image Analysis. IbPRIA, Sanches, J.M., Micó, L., and Cardoso, J.S., Eds., Lecture Notes in Computer Science, vol. 7887, Berlin: Springer, 2013, pp. 108–116. https://doi.org/10.1007/978-3-642-38628-2_12

    Book  Google Scholar 

  25. Hassan, A.R. and Hassan Bhuiyan, M.I., Automatic sleep scoring using statistical features in the EMD domain and ensemble methods, Biocybernetics Biomed. Eng., 2016, vol. 36, no. 1, pp. 248–255. https://doi.org/10.1016/j.bbe.2015.11.001

    Article  Google Scholar 

  26. Hassan, A.R., Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting, Biomed. Signal Process. Control, 2016, vol. 29, pp. 22–30. https://doi.org/10.1016/j.bspc.2016.05.009

    Article  Google Scholar 

  27. Ting, K.M. and Witten, I.H., Issues in stacked generalization, J. Artif. Intell. Res., 1999, vol. 10, pp. 271–289. https://doi.org/10.1613/jair.594

    Article  MATH  Google Scholar 

  28. Witten, I.H., Frank, E., and Hall, M.A., Data Mining: Practical Machine Learning Tools and Techniques, Amsterdam: Elsevier Science, 2005, 2nd ed.

    MATH  Google Scholar 

  29. Džeroski, S. and Ženko, B., Is combining classifiers with stacking better than selecting the best one?, Mach. Learn., 2004, vol. 54, no. 3, pp. 255–273. https://doi.org/10.1023/b:mach.0000015881.36452.6e

    Article  MATH  Google Scholar 

  30. Ram, S. and Gupta, S., Building machine learning based diseases diagnosis system considering various features of datasets, Emerging Trends in Expert Applications and Security, Rathore, V., Worring, M., Mishra, D., Joshi, A., and Maheshwari, S., Eds., Advances in Intelligent Systems and Computing, vol. 841, Singapore: Springer, 2019, pp. 147–155. https://doi.org/10.1007/978-981-13-2285-3_19

  31. Mihaylov, I., Nisheva, M., and Vassilev, D., Machine learning techniques for survival time prediction in breast cancer, Artificial Intelligence: Methodology, Systems, and Applications, Agre, G., van Genabith, J., and Declerck, T., Eds., Lecture Notes in Computer Science, vol. 11089, Cham: Springer, 2018, pp. 186–194. https://doi.org/10.1007/978-3-319-99344-7_17

  32. Selvathi, D. and Aarthy Poornila, A., Deep learning techniques for breast cancer detection using medical image analysis, Biologically Rationalized Computing Techniques for Image Processing Applications, Hemanth, J. and Balas, V., Eds., Lecture Notes in Computational Vision and Biomechanics, vol. 25, Cham: Springer, 2018, pp. 159–186. https://doi.org/10.1007/978-3-319-61316-1_8

  33. Li, X., Qin, G., He, Q., Sun, L., Zeng, H., He, Z., Chen, W., Zhen, X., and Zhou, L., Digital breast tomosynthesis versus digital mammography: Integration of image modalities enhances deep learning-based breast mass classification, Eur. Radiology, 2020, vol. 30, no. 2, pp. 778–788. https://doi.org/10.1007/s00330-019-06457-5

    Article  Google Scholar 

  34. Chougrad, H., Zouaki, H., and Alheyane, O., Deep convolutional neural networks for breast cancer screening, Comput. Methods Programs Biomedicine, 2018, vol. 157, pp. 19–30. https://doi.org/10.1016/j.cmpb.2018.01.011

    Article  Google Scholar 

  35. Kate, R.J. and Nadig, R., Stage-specific predictive models for breast cancer survivability, Int. J. Med. Inf., 2017, vol. 97, pp. 304–311. https://doi.org/10.1016/j.ijmedinf.2016.11.001

    Article  Google Scholar 

  36. Asri, H., Mousannif, H., Al Moatassime, H., and Noel, T., Using machine learning algorithms for breast cancer risk prediction and diagnosis, Procedia Comput. Sci., 2016, vol. 83, pp. 1064–1069. https://doi.org/10.1016/j.procs.2016.04.224

    Article  Google Scholar 

  37. Samala, R.K., Chan, H.-P., Hadjiiski, L., Helvie, M.A., Richter, C.D., and Cha, K.H., Breast cancer diagnosis in digital breast tomosynthesis: Effects of training sample size on multi-stage transfer learning using deep neural nets, IEEE Trans. Med. Imaging, 2019, vol. 38, no. 3, pp. 686–696. https://doi.org/10.1109/tmi.2018.2870343

    Article  Google Scholar 

  38. U.I.M.L. Repository, Machine Learning Repository, 2010. http://archive.ics.uci.edu/ml/index.php. Cited July 19, 2022.

  39. Shukla, S., Jain, P.K., Babu, Ch.R., and Pamula, R., A multivariate regression model for identifying, analyzing and predicting crimes, Wireless Personal Commun., 2020, vol. 113, no. 4, pp. 2447–2461. https://doi.org/10.1007/s11277-020-07335-w

    Article  Google Scholar 

  40. Friedman, N., Geiger, D., and Goldszmidt, M., Bayesian network classifiers, Mach. Learn., 1997, vol. 29, no. 2/3, pp. 131–163. https://doi.org/10.1023/a:1007465528199

    Article  MATH  Google Scholar 

  41. Shastri, S., Kour, P., Kumar, S., Singh, K., Sharma, A., and Mansotra, V., A nested stacking ensemble model for predicting districts with high and low maternal mortality ratio (MMR) in India, Int. J. Inf. Technol., 2021, vol. 13, no. 2, pp. 433–446. https://doi.org/10.1007/s41870-020-00560-3

    Article  Google Scholar 

  42. Saito, T. and Rehmsmeier, M., The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets, PLoS ONE, 2015, vol. 10, no. 3, p. e0118432. https://doi.org/10.1371/journal.pone.0118432

    Article  Google Scholar 

  43. Abdar, M., Zomorodi-Moghadam, M., Zhou, X., Gururajan, R., Tao, X., Barua, P., and Gururajan, R., A new nested ensemble technique for automated diagnosis of breast cancer, Pattern Recognit. Lett., 2020, vol. 132, pp. 123–131. https://doi.org/10.1016/j.patrec.2018.11.004

    Article  Google Scholar 

  44. Lu, H., Wang, H., and Yoon, S.W., A dynamic gradient boosting machine using genetic optimizer for practical breast cancer prognosis, Expert Syst. Appl., 2019, vol. 116, pp. 340–350. https://doi.org/10.1016/j.eswa.2018.08.040

    Article  Google Scholar 

  45. Maglogiannis, I., Zafiropoulos, E., and Anagnostopoulos, I., An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers, Appl. Intell., 2009, vol. 30, no. 1, pp. 24–36. https://doi.org/10.1007/s10489-007-0073-z

    Article  Google Scholar 

  46. Sáez, J., Derrac, J., Luengo, J., and Herrera, F., Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers, Pattern Recognit., 2014, vol. 47, no. 12, pp. 3941–3948. https://doi.org/10.1016/j.patcog.2014.06.012

    Article  Google Scholar 

  47. Lim, C.K. and Chan, C.S., A weighted inference engine based on interval-valued fuzzy relational theory, Expert Syst. Appl., 2015, vol. 42, no. 7, pp. 3410–3419. https://doi.org/10.1016/j.eswa.2014.12.025

    Article  Google Scholar 

  48. Aličković, E. and Subasi, A., Breast cancer diagnosis using GA feature selection and Rotation Forest, Neural Comput. Appl., 2017, vol. 28, no. 4, pp. 753–763. https://doi.org/10.1007/s00521-015-2103-9

    Article  Google Scholar 

  49. Nilashi, M., Ibrahim, O., Ahmadi, H., and Shahmoradi, L., A knowledge-based system for breast cancer classification using fuzzy logic method, Telematics Inf., 2017, vol. 34, no. 4, pp. 133–144. https://doi.org/10.1016/j.tele.2017.01.007

    Article  Google Scholar 

  50. Sanchez, A., Soguero-Ruiz, C., Mora-Jiménez, I., Rivas-Flores, F.J., Lehmann, D., and Rubio-Sánchez, M., Scaled radial axes for interactive visual feature selection: A case study for analyzing chronic conditions, Expert Syst. Appl., 2018, vol. 100, pp. 182–196. https://doi.org/10.1016/j.eswa.2018.01.054

    Article  Google Scholar 

  51. Suresh, A. and Varatharajan, R., Recognition of pivotal instances from uneven set boundary during classification, Multimedia Tools Appl., 2018, vol. 77, no. 20, pp. 27075–27088. https://doi.org/10.1007/s11042-018-5905-9

    Article  Google Scholar 

  52. Alshayeji, M.H., Ellethy, H., Abed, S., and Gupta, R., Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach, Biomed. Signal Process. Control, 2022, vol. 71, p. 103141. https://doi.org/10.1016/j.bspc.2021.103141

    Article  Google Scholar 

  53. Sannasi Chakravarthy, S.R., Bharanidharan, N., and Rajaguru, H., Deep learning-based metaheuristic weighted k-nearest neighbor algorithm for the severity classification of breast cancer, IRBM, 2023, vol. 44, no. 3, p. 100749. https://doi.org/10.1016/j.irbm.2022.100749

    Article  Google Scholar 

Download references

ACKNOWLEDGMENTS

This research work is dedicated to patients suffering from breast cancer.

Author information

Authors and Affiliations

  1. Department of Computer Science and IT, Central University of Jammu, 181143, Jammu and Kashmir, India

    Kuljeet Singh

  2. Department of Computer Science and IT, University of Jammu, 180006, Jammu and Kashmir, India

    Sourabh Shastri, Sachin Kumar & Vibhakar Mansotra

Authors
  1. Kuljeet Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sourabh Shastri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sachin Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vibhakar Mansotra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kuljeet Singh.

Ethics declarations

The authors declare that they have no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuljeet Singh, Shastri, S., Kumar, S. et al. BC-Net: Early Diagnostics of Breast Cancer Using Nested Ensemble Technique of Machine Learning. Aut. Control Comp. Sci. 57, 646–659 (2023). https://doi.org/10.3103/S0146411623060093

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411623060093

Keywords:

Search

Navigation