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A Comparative Study of Computational Intelligence for Identification of Breast Cancer

  • Divyue SharmaEmail author
  • Parva JainEmail author
  • Dilip Kumar ChoubeyEmail author
Conference paper
  • 38 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1240)

Abstract

Breast cancer is a type of invasive cancer that occurs in women. Breast cancer accounts for 18% of all cancer related deaths among women according to World Health Organization. After Lung Cancer, breast cancer is the leading cause of death of women in India. Due to inaccessibility, especially in rural areas, it is impossible for everyone to get diagnosed in time. If breast cancer is detected at an early stage, the doctor will be aided in suggesting an efficient way to proceed with the treatment of the patient, thus reducing the mortality rate and medical expenses. So, in this paper a comparative study on machine learning and computational intelligence techniques has been performed to optimize the process and achieve better accuracy and precision. The focus of this review article is to survey several articles existing on breast cancer majorly on Wisconsin dataset which is obtained from UCI repository. This review article has been concluded with suggestions for future directions.

Keywords

Machine learning Soft computing KNN CNN Naive Bayes K-means Logistic Regression SVM (Support Vector Machine) Random Forest 

References

  1. 1.
    Bayrak, E.A., Kırcı, P., Ensari, T.: Comparison of machine learning methods for breast cancer diagnosis. In: Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey, pp. 1–3 (2019)Google Scholar
  2. 2.
    Bharat, A., Pooja, N., Reddy, R.A.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. In: 3rd International Conference on Circuits, Control, Communication and Computing (I4C), Bangalore, India, pp. 1–4 (2018)Google Scholar
  3. 3.
    Nemissi, M., Salah, H., Seridi, H.: Breast cancer diagnosis using an enhanced extreme learning machine based-neural network. In: International Conference on Signal, Image, Vision and their Applications (SIVA), Guelma, Algeria, pp. 1–4 (2018)Google Scholar
  4. 4.
    Bhat, J.A., George, V., Malik, B.: Cloud computing with machine learning could help us in the early diagnosis of breast cancer. In: Second International Conference on Advances in Computing and Communication Engineering, Dehradun, pp. 644–648 (2015)Google Scholar
  5. 5.
    Sultan, L.R., Schultz, S.M., Cary, T.W., Sehgal, C.M.: Machine learning to improve breast cancer diagnosis by multimodal ultrasound. In: IEEE International Ultrasonics Symposium (IUS), Kobe, pp. 1–4 (2018)Google Scholar
  6. 6.
    Sharma, S., Aggarwal, A., Choudhury, T.: Breast cancer detection using machine learning algorithms. In: International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), Belgaum, India, pp. 114–118 (2018)Google Scholar
  7. 7.
    Bazazeh, D., Shubair, R.: Comparative study of machine learning algorithms for breast cancer detection and diagnosis. In: 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), Ras Al Khaimah, pp. 1–4 (2016)Google Scholar
  8. 8.
    Khuriwal, N., Mishra, N.: Breast cancer diagnosis using deep learning algorithm. In: International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida, UP, India, pp. 98–103 (2018)Google Scholar
  9. 9.
    Lu, Y., Li, J.-Y., Su, Y.-T., Liu, A.-A.: A review of breast cancer detection in medical images. In: IEEE Visual Communications and Image Processing (VCIP), Taichung, Taiwan, pp. 1–4 (2018)Google Scholar
  10. 10.
    Osareh, A., Shadgar, B.: Machine learning techniques to diagnose breast cancer. In: 5th International Symposium on Health Informatics and Bioinformatics, Antalya, pp. 114–120 (2010)Google Scholar
  11. 11.
    Islam, M., Iqbal, H., Haque, R., Hasan, K.: Prediction of breast cancer using support vector machine and K-nearest neighbors. In: IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 226–229 (2017)Google Scholar
  12. 12.
    Polat, K., Sentürk, U.: A novel ML approach to prediction of breast cancer: combining of mad normalization, KMC based feature weighting and AdaBoostM1 classifier. In: 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, pp. 1–4 (2018)Google Scholar
  13. 13.
    Choubey, D.K., Paul, S., Sandilya, S., Dhandhania, V.K.: Implementation and analysis of classification algorithms for diabetes. Curr. Med. Imaging Rev. (2018, in Press)Google Scholar
  14. 14.
    Choubey, D.K., Kumar, P., Tripathi, S., Kumar, S.: Performance evaluation of classification methods with PCA and PSO for diabetes. Netw. Model. Anal. Health Inform. Bioinform. 9 (2020). Article number: 5.  https://doi.org/10.1007/s13721-019-0210-8
  15. 15.
    Sharma, A., Kulshrestha, S., Daniel, S.: Machine learning approaches for breast cancer diagnosis and prognosis. In: International Conference on Soft Computing and its Engineering Applications (icSoftComp), Changa, pp. 1–5 (2017)Google Scholar
  16. 16.
    Saleh, D.T., Attia, A., Shaker, O.: Studying combined breast cancer biomarkers using machine learning techniques. In: IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Herlany, pp. 247–251 (2016)Google Scholar
  17. 17.
    Choubey, D.K., Tripathi, S., Kumar, P., Shukla, V., Dhandhania, V.K.: Classification of diabetes by kernel based SVM with PSO. Recent Patents Comput. Sci. (2019, in Press)Google Scholar
  18. 18.
    Choubey, D.K., Kumar, M., Shukla, V., Tripathi, S., Dhandhania, V.K.: Comparative analysis of classification methods with PCA and LDA for diabetes. Curr. Diabetes Rev. (2020, Accepted)Google Scholar
  19. 19.
    Gupta, M., Gupta, B.: A comparative study of breast cancer diagnosis using supervised machine learning techniques. In: Second International Conference on Computing Methodologies and Communication (ICCMC), Erode, pp. 997–1002 (2018)Google Scholar
  20. 20.
    Khuriwal, N., Mishra, N.: Breast cancer detection from histopathological images using deep learning. In: 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India, pp. 1–4 (2018)Google Scholar
  21. 21.
    Amrane, M., Oukid, S., Gagaoua, I., Ensarİ, T.: Breast cancer classification using machine learning. In: 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), Istanbul, pp. 1–4 (2018)Google Scholar
  22. 22.
    Adel, M., Kotb, A., Farag, O., Darweesh, M.S., Mostafa, H.: Breast cancer diagnosis using image processing and machine learning for elastography images. In: 8th International Conference on Modern Circuits and Systems Technologies (MOCAST), Thessaloniki, Greece, pp. 1–4 (2019)Google Scholar
  23. 23.
    Choubey, D.K., Paul, S.: GA_RBF NN: a classification system for diabetes. Int. J. Biomed. Eng. Technol. (IJBET) 23(1), 71–93 (2017)CrossRefGoogle Scholar
  24. 24.
    Choubey, D.K., Paul, S.: Classification techniques for diagnosis of diabetes: a review. Int. J. Biomed. Eng. Technol. (IJBET) 21(1), 15–39 (2016)CrossRefGoogle Scholar
  25. 25.
    Choubey, K., Paul, S.: GA_MLP NN: a hybrid intelligent system for diabetes disease diagnosis. Int. J. Intell. Syst. Appl. (IJISA) 8(1), 49–59 (2016)Google Scholar
  26. 26.
    Bala, K., Choubey, D.K., Paul, S.: Soft computing and data mining techniques for thunderstorms and lightning prediction: a survey. In: International Conference of Electronics, Communication and Aerospace Technology (ICECA 2017), 20–22 April, 2017, vol. 1, pp. 42–46. IEEE (2017)Google Scholar
  27. 27.
    Bala, K., Choubey, D.K., Paul, S., Lala, M.G.N.: Classification techniques for thunderstorms and lightning prediction-a survey. In: Soft Computing-Based Nonlinear Control Systems Design, pp. 1–17. IGI Global (2018)Google Scholar
  28. 28.
    World Health Organization: WHO position paper on mammography screening. WHO Library (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Computer Science and Engineering (SCOPE)Vellore Institute of TechnologyVelloreIndia

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