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International Conference on Advanced Informatics for Computing Research

ICAICR 2019: Advanced Informatics for Computing Research pp 3–11Cite as

Naïve Bayes Model Based Improved K-Nearest Neighbor Classifier for Breast Cancer Prediction

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1075))

Abstract

Breast cancer is one of the major cancers that is common to women all over the world. Though, the cancer is curable and can be prevented if it is detected in early stages. In medical science, lots of different strategies have been developed to detect and diagnose the cancer patients. Data mining techniques are no far behind and are widely used to extract information from large databases of the cancer patients to discover some patterns making decisions. Classification is one of the data mining techniques that can be used to classify the data in two stages i.e. benign or malignant. This paper presents the Naïve Bayes improved K-Nearest Neighbor method (NBKNN) for breast cancer prediction and compares the results with traditional classifiers like traditional K-nearest Neighbor and naïve Bayes. In the experiments, the standard dataset used is taken from UCI repository. Sensitivity and specificity have been used as accuracy measures for comparing the results. Experimental results show that proposed classifier is better than traditional classifiers.

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References

  1. www.breastcancerindia.net/statistics/

  2. Ratanachaikanont, T.: Clinical breast examination and its relevance to diagnosis of palpable breast lesion. J. Med. Assoc. Thailand 88(4), 505–507 (2005)

    Google Scholar 

  3. Nover, A.B., Jagtap, S., Anjum, W., et al.: Modern breast cancer detection: a technological review. Int. J. Biomed. Imaging, 2009, 14 p. (2009). Article ID 902326, https://doi.org/10.1155/2009/902326

    Article  Google Scholar 

  4. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Fransisco (2005)

    MATH  Google Scholar 

  5. Delen, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34(2), 113–127 (2005)

    Article  Google Scholar 

  6. Gupta, S., Kumar, D., Sharma, A.: Data mining classification techniques applied for breast cancer diagnosis and prognosis. Indian J. Comput. Sci. Eng. (IJCSE) 2(2), 188–195 (2011)

    Google Scholar 

  7. Kuo, W.-J., Chang, R.-F., Chen, D.-R., Lee, C.C.: Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images. Breast Cancer Res. Treat. 66(1), 51–57 (2001)

    Article  Google Scholar 

  8. Chaurasia, V., Pal, S.: Data mining techniques: to predict and resolve breast cancer survivability. Int. J. Comput. Sci. Mob. Comput. 3(1), 10–22 (2014)

    Google Scholar 

  9. Venkatesan, E., Velmurugan, T.: Performance analysis of decision tree algorithms for breast cancer classification. Indian J. Sci. Technol. 8(29), 1–8 (2015)

    Article  Google Scholar 

  10. Kharya, S., Agrawal, S., Soni, S.: Naive Bayes classifiers: a probabilistic detection model for breast cancer. Int. J. Comput. Appl. 92(10), 0975–8887 (2014)

    Google Scholar 

  11. Williams, K., Idowu, P.A., Balogun, J.A., Oluwaranti, A.I.: Breast cancer risk prediction using data mining classification techniques. Trans. Netw. Commun. 3(2), 01–11 (2015)

    Google Scholar 

  12. Tomar, D., Agarwal, S.: A survey on data mining approaches for healthcare. Int. J. Bio-Sci. Bio-Technol. 5(5), 241–266 (2013)

    Article  Google Scholar 

  13. Ramadevi, G.N., Rani, K.U., Lavanya, D.: Importance of feature extraction for classification of breast cancer datasets—a study. Int. J. Sci. Innovative Math. Res. 3(2), 368–763 (2015)

    Google Scholar 

  14. Majid, A., Ali, S., Iqbal, M., Kausar, N.: Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines. Comput. Meth. Programs Biomed. 113(3), 792–808 (2014)

    Article  Google Scholar 

  15. Ahmad, L.G., Eshlaghy, A.T., Poorebrahimi, A., Ebrahimi, M., Razavi, A.R.: Using three machine learning techniques for predicting breast cancer recurrence. J. Health Med. Inform. 4(124), 3 (2013)

    Google Scholar 

  16. Ramadevi, G.N., Rani, K.U., Lavanya, D.: Evaluation of classifiers performance using resampling on breast cancer data. Int. J. Sci. Eng. Res. 6(2) (2015)

    Google Scholar 

  17. Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 160, 3–24 (2007)

    Google Scholar 

  18. Mangasarian, O.L., Wolberg, W.H.: Cancer diagnosis via linear programming. SIAM News 23(5), 1–18 (1990)

    Google Scholar 

  19. Wolberg, W.H., Mangasarian, O.L.: Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In: Proceedings of the National Academy of Sciences, U.S.A., vol. 87, pp. 9193–9196, December 1990

    Article  Google Scholar 

  20. Islam, Md.M., Iqbal, H., Haque, Md.R., Hasan, Md.K.: Prediction of breast cancer using support vector machine and K-nearest neighbors. In: 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 226–229. IEEE (2017)

    Google Scholar 

  21. Obaid, O.I., Mohammed, M.A., Ghani, M.K.A., Mostafa, S.A., Taha, F.: Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer. Int. J. Eng. Technol. 7(4.36), 160–166 (2018)

    Google Scholar 

  22. Yue, W., Wang, Z., Chen, H., Payne, A., Liu, X.: Machine learning with applications in breast cancer diagnosis and prognosis. Designs 2, 13 (2018). https://doi.org/10.3390/designs2020013

    Article  Google Scholar 

  23. Gupta, A., Kaushik, B.N.: Feature selection from biological database for breast cancer prediction and detection using machine learning classifier. J. Artif. Intell. 11, 55–64 (2018)

    Article  Google Scholar 

  24. Chawla, S., Kumar, R., Aggarwal, E., Swain, S.: Breast cancer detection using K-nearest neighbor algorithm. Int. J. Comput. Intell. IoT 2(4) (2018)

    Google Scholar 

  25. Cherif, W.: Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis. Proc. Comput. Sci. 127, 293–299 (2018)

    Article  Google Scholar 

  26. Sun, J., et al.: Predicting medical conditions using k-nearest neighbors. University of Nevada, Las Vegas (2017)

    Google Scholar 

  27. Alarabeyyat, A., Alhanahnah, M.: Breast cancer detection using k nearest neighbor machine learning algorithm. In: 2016 9th International Conference on Developments in eSystems Engineering (DeSE), pp. 35–39. IEEE (2016)

    Google Scholar 

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Authors and Affiliations

  1. Government Boys Senior Secondary School, Holambi Kalan, Delhi, India

    Sonia Goyal &  Maheshwar

Authors
  1. Sonia Goyal
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  2. Maheshwar
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Corresponding author

Correspondence to Maheshwar .

Editor information

Editors and Affiliations

  1. Papua New Guinea University of Technology, Lae, Papua New Guinea

    Ashish Kumar Luhach

  2. Computer Science Department, Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  3. Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Kamarul Bin Ghazali Hawari

  4. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  5. Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Canada

    Pawan Lingras

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© 2019 Springer Nature Singapore Pte Ltd.

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Goyal, S., Maheshwar (2019). Naïve Bayes Model Based Improved K-Nearest Neighbor Classifier for Breast Cancer Prediction. In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-15-0108-1_1

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  • DOI: https://doi.org/10.1007/978-981-15-0108-1_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0107-4

  • Online ISBN: 978-981-15-0108-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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