Skip to main content
SpringerLink
Log in

Automated Diagnosis of Breast Cancer: An Ensemble Approach

  • Conference paper
  • First Online:
Advances in Data Computing, Communication and Security

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 106))

Abstract

Breast cancer is the most dominant cancer among women and has caused millions of deaths in the world. Automated learning techniques make a significant contribution to cancer prediction studies. In this study, we focused on ensemble learning techniques for breast cancer diagnosis prediction. The dataset used in this work is publicly available at the University of California, Irvine repository. To achieve more consistent results, our study advocates the use of ensemble approaches. The proposed methodology increased prediction accuracy from 76% with the “RBF” kernel to 81% with ensemble learning. The simulation results prove that the proposed model can serve as a cancer prediction model. This paper presents an ensemble approach to integrate the simulation results of multiple classification models.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. J. Ferlay, Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods 1–13 (2018). https://doi.org/10.1002/ijc.31937

  2. T.J. Key, P.K. Verkasalo, E. Banks, Rev. Epidemiol. Breast Cancer 44, 133–140 (1865)

    Google Scholar 

  3. H. Kennecke, R. Yerushalmi, R. Woods, M.C.U. Cheang, D. Voduc, C.H. Speers, … Gelmon, K. J. Clin. Oncol. Metastatic Behav. Breast Cancer Subtypes 28(20), 3271–3277. https://doi.org/10.1200/JCO.2009.25.9820

  4. B.O. Anderson, S. Braun, S. Lim, R.A. Smith, S. Taplin, D.B. Thomas, … D. Panel, Early Detection of Breast Cancer in Countries with Limited Resources (2003)

    Google Scholar 

  5. K. Jp, G. Pc, Regular self-examination or clinical examination for early detection of breast cancer (2) (2008)

    Google Scholar 

  6. R. Sumbaly, Diagnosis of breast cancer using decision tree data mining technique 98(10), 16–24 (2014)

    Google Scholar 

  7. Y. Xiao, J. Wu, Z. Lin, X. Zhao, A deep learning-based multi-model ensemble method for cancer prediction. Comput. Methods Prog. Biomed. 153, 1–9 (2020). https://doi.org/10.1016/j.cmpb.2017.09.005

    Article  Google Scholar 

  8. K. Kourou, T.P. Exarchos, K.P. Exarchos, M.V. Karamouzis, D.I. Fotiadis, Machine learning applications in cancer prognosis and prediction. CSBJ 13, 8–17 (2015). https://doi.org/10.1016/j.csbj.2014.11.005

    Article  Google Scholar 

  9. Y. Chen, W. Ke, H. Chiu, Risk classification of cancer survival using ANN with gene expression data from multiple laboratories. Comput. Biol. Med. 48, 1–7 (2014). https://doi.org/10.1016/j.compbiomed.2014.02.006

    Article  Google Scholar 

  10. A.B. Levine, C. Schlosser, J. Grewal, R. Coope, S.J.M. Jones, S. Yip, Rise of the machines: advances in deep learning for cancer diagnosis. TRENDS in CANCER, 1–13 (2019). https://doi.org/10.1016/j.trecan.2019.02.002

  11. M. Li, Z.H. Zhou, Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 37(6), 1088–1098 (2007). https://doi.org/10.1109/TSMCA.2007.904745

    Article  Google Scholar 

  12. C.S. Ong, A.J. Smola, R.C. Williamson, Learning the kernel with hyperkernels. J. Mach. Learn. Res. 6 (2005)

    Google Scholar 

  13. S. Gupta, M.K. Gupta, Computational prediction of cervical cancer diagnosis using ensemble-based classification algorithm. Comput. J. (2021)

    Google Scholar 

  14. L. Parthiban, Abnormality detection using weighed particle swarm optimization and smooth support vector machine 28(11), 4749–4751 (2017)

    Google Scholar 

  15. N. Shukla, M. Hagenbuchner, K.T. Win, J. Yang, PT US CR. Comput. Methods Prog. Biomed. (2017). https://doi.org/10.1016/j.cmpb.2017.12.011

  16. Setiono, R, Generating concise and accurate classification rules for breast cancer diagnosis 65 (n.d.)

    Google Scholar 

  17. S. Bashir, U. Qamar, F. Hassan, Heterogeneous classifiers fusion for dynamic breast cancer diagnosis using weighted vote based ensemble (2014). https://doi.org/10.1007/s11135-014-0090-z

    Article  Google Scholar 

  18. W.H. Wolberg, Multisurface method of pattern separation for medical diagnosis applied to breast cytology 87(December), 9193–9196 (1990)

    Google Scholar 

  19. J.R. Quinlan, Improved use of continuous attributes in C4 5(4)(1996), 77–90 (2006)

    Google Scholar 

  20. T. Masters, Probabilistic neural networks. practical neural network recipies in C++ 3, 201–222 (1993). https://doi.org/10.1016/b978-0-08-051433-8.50017-3

  21. M. Seera, C.P. Lim, A hybrid intelligent system for medical data classification. Expert Syst. Appl. (2013). https://doi.org/10.1016/j.eswa.2013.09.022

    Article  Google Scholar 

  22. A. Purwar, S.K. Singh, Expert systems with applications hybrid prediction model with missing value imputation for medical data. Expert Syst. Appl. 42(13), 5621–5631 (2015). https://doi.org/10.1016/j.eswa.2015.02.050

    Article  Google Scholar 

  23. A.M. Nilashi, O. Ibrahim, An analytical method for diseases prediction using machine learning techniques. Comput. Chem. Eng. (2017). https://doi.org/10.1016/j.compchemeng.2017.06.011

    Article  Google Scholar 

  24. J.R. Quinlan, Simplifying decision trees. Int. J. Hum. Comput. Stud. 51(2), 497–510 (1999). https://doi.org/10.1006/ijhc.1987.0321

    Article  Google Scholar 

  25. D. Nauck, R. Kruse, Obtaining interpretable fuzzy classification rules from medical data 16, 149–169 (1999)

    Google Scholar 

  26. J. Abonyi, F. Szeifert, Supervised fuzzy clustering for the identification of fuzzy classifiers 24, 2195–2207 (2003). https://doi.org/10.1016/S0167-8655(03)00047-3

    Article  Google Scholar 

  27. K. Polat, S. Güneş, Breast cancer diagnosis using least square support vector machine. Dig. Sig. Process. 17(4), 694–701 (2007)

    Article  Google Scholar 

  28. M.F. Akay, Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst. Appl. 36(2), 3240–3247 (2009). https://doi.org/10.1016/j.eswa.2008.01.009

    Article  Google Scholar 

  29. Y. Peng, Z. Wu, J. Jiang, A novel feature selection approach for biomedical data classification. J. Biomed. Inform. 43(1), 15–23 (2010). https://doi.org/10.1016/j.jbi.2009.07.008

    Article  Google Scholar 

  30. G.I. Salama, M.B. Abdelhalim, M.A. Zeid, Using multi-classifiers (2012)

    Google Scholar 

  31. U.K. Kumar, M.B.S. Nikhil, K. Sumangali, Prediction of breast cancer using voting classifier technique 108–114 (2017)

    Google Scholar 

  32. C. Diagnosis, Machine learning with applications in breast cancer diagnosis and prognosis. 1–17 (2018). https://doi.org/10.3390/designs2020013

  33. I. Salman, Impact of metaheuristic iteration on artificial neural (2018). https://doi.org/10.3390/pr6050057

    Article  Google Scholar 

  34. Saygili, A.: Classification and diagnostic prediction of breast cancers via different classification and diagnostic prediction of breast cancers via different classifiers (December 2018) (2019)

    Google Scholar 

  35. S. Gupta, M.K. Gupta, A comprehensive data‐level investigation of cancer diagnosis on imbalanced data. Comput. Intell. (2021)

    Google Scholar 

  36. A. Celisse, A survey of cross-validation procedures for model selection ∗. 4, 40–79 (2010). https://doi.org/10.1214/09-SS054

  37. S. Gupta, M.K. Gupta, R. Kumar, A Novel Multi-Neural Ensemble Approach for Cancer Diagnosis. Appl. Artif. Intell. 1–36 (2021). https://doi.org/10.1080/08839514.2021.2018182

  38. S. Gupta, M.K. Gupta, Computational model for prediction of malignant mesothelioma diagnosis. The Comput. J. (2021). https://doi.org/10.1093/comjnl/bxab146

  39. S. Gupta, M. Kumar. Prostate cancer prognosis using multi-layer perceptron and class balancing techniques. In 2021 Thirteenth Int. Conf. Contemp. Comput. (IC3-2021), 1–6 (2021). https://doi.org/10.1145/3474124.3474125

  40. S. Gupta and M. Gupta, Deep learning for brain tumor segmentation using magnetic resonance Images. IEEE Conf. Comput. Intell. Bioinf. Comput. Biol. (CIBCB), 1–6 (2021). https://doi.org/10.1109/CIBCB49929.2021.9562890

  41. S.-B. Cho, H.-H. Won, Machine learning in DNA microarray analysis for cancer classification. in Proceedings of the First Asia-Pacific Bioinformatics Conference on Bioinformatics 2003, vol. 19 (2003), pp. 189–198

    Google Scholar 

  42. H.O. Ilhan, E. Celik, The mesothelioma disease diagnosis with artificial intelligence methods. in Application of Information and Communication Technologies, AICT 2016—Conference Proceedings (2017). https://doi.org/10.1109/ICAICT.2016.7991825

  43. P. Geurts, D. Ernst, L. Wehenkel, Extremely randomized trees, 3–42 (2006). https://doi.org/10.1007/s10994-006-6226-1

  44. S. Gupta, M.K. Gupta, A comparative analysis of deep learning approaches for predicting breast cancer survivability. Arch. Comput. Methods Eng. 1–17 (2021). https://doi.org/10.1007/s11831-021-09679-3

  45. S. Gupta, A. Gupta, Y. Kumar, Artificial intelligence techniques in Cancer research: Opportunities and challenges. In 2021 Int. Conf. Technol. Advancements and Innovations (ICTAI). 411–416. (2021). IEEE. https://doi.org/10.1109/ICTAI53825.2021.9673174

  46. S. Gupta, Y. Kumar, Cancer prognosis using artificial intelligence-based techniques. SN Comput. Sci. 3(1), 1–8 (2022). https://doi.org/10.1007/s42979-021-00964-3

  47. Y. Kumar, K. Sood, S. Kaul, R. Vasuja, R., Big data analytics and its benefits in healthcare. In Big Data Analytics in Healthcare (pp. 3–21). (2021) Springer, Cham

    Google Scholar 

  48. Y. Kumar, Recent advancement of machine learning and deep learning in the field of healthcare system. In Comput. Intell. Mach. Learn. Healthcare Inform. 7–98 (2021)

    Google Scholar 

  49. Y. Kumar, R. Singla, Federated learning systems for healthcare: Perspective and recent progress. In: Rehman M.H.., Gaber M.M. (eds) Federated Learning Systems. Stud. Comput. Intell. 965. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70604-3_6

  50. Y. Kumar, S. Gupta, R. Singla, Y.C. Hu, A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Arch. Comput. Methods Eng. 1–28 (2021)

    Google Scholar 

  51. Y. Kumar, S. Gupta, W. Singh, A novel deep transfer learning models for recognition of birds sounds in different environment. Soft. Comput. (2022). https://doi.org/10.1007/s00500-021-06640-1

Download references

Conflict of Interest

The authors declare that they have no conflict of interests to disclose.

Author information

Authors and Affiliations

  1. Model Institute of Engineering and Technology, Kot bhalwal, Jammu and Kashmir, India

    Surbhi Gupta

  2. Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India

    Surbhi Gupta

Authors
  1. Surbhi Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, India

    Pankaj Verma

  2. Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, India

    Chhagan Charan

  3. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  4. Department of Electrical and Computer Engineering, Oakland University, Rochester, MI, USA

    Subramaniam Ganesan

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gupta, S. (2022). Automated Diagnosis of Breast Cancer: An Ensemble Approach. In: Verma, P., Charan, C., Fernando, X., Ganesan, S. (eds) Advances in Data Computing, Communication and Security. Lecture Notes on Data Engineering and Communications Technologies, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-16-8403-6_18

Download citation

Publish with us

Policies and ethics

Search

Navigation