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Volume: 08 Issue: 02 | Feb 2021 www.irjet.net p-ISSN: 2395-0072

[16] William H Wolberg. 1992. Breast cancer with Different Distances and Classification

Wisconsin (Original) data set.UCI Machine Learning Rules. International Journal of Computer Applications,

Repository [http://archive. ics. uci. edu/ml/] (1992). 62, 1-5. [17] Karapinar Senturk, Zehra & Kara, Resul. (2014). [26] Sumbaly, Ronak & Vishnusri, N. & Jeyalatha, s.

Breast Cancer Diagnosis Via Data Mining: (2014). Diagnosis of Breast Cancer using Decision

Performance Analysis of Seven Different Algorithms. Tree Data Mining Technique. International Journal of

Computer Science & Engineering: An International Computer Applications. 98. 16-24. 10.5120/17219-

Journal. 4. 35-46. 10.5121/cseij.2014.4104. 7456. [18] You, H., & Rumbe, G. (2010). Comparative Study of [27] Hazra, A., Mandal, S.K., & Gupta, A. (2016). Study

Classification Techniques on Breast Cancer FNA and Analysis of Breast Cancer Cell Detection using

Biopsy Data. Int. J. Interact. Multim. Artif. Intell., 1, 5- Naïve Bayes, SVM and Ensemble 12. Algorithms. International Journal of Computer [19] Aruna, S & Rajagopalan, Dr & Nandakishore, L. Applications, 145, 39-45. (2011). Knowledge based analysis of various statistical tools in detecting breast cancer. Computer BIOGRAPHY

Science & Information Technology. 2. Apratim Sadhu is currently pursuing B.Tech 10.5121/csit.2011.1205. in Computer Science Engineering from [20] William H Wolberg, W Nick Street, and Olvi L Chandigarh University, Mohali, India. His

Mangasarian. 1992. Breast cancer Wisconsin area of specialization in the under-graduate (diagnostic) data set. UCI degree is Artificial Intelligence and Machine

Machine Learning Repository [http://archive. ics. uci. Learning. He is a rank holder in 19th National [21] edu/ml/] (1992). Zafiropoulos E., Maglogiannis I., Children Science Congress. He article on Machine Learning. has written 1 research

Anagnostopoulos I. (2006) A Support Vector

Machine Approach to Breast Cancer Diagnosis and

Prognosis. In: Maglogiannis I., Karpouzis K., Bramer

M. (eds) Artificial Intelligence Applications and

Innovations. AIAI 2006. IFIP International

Federation for Information Processing, vol 204.

Springer, Boston, MA . https://doi.org/10.1007/0387-34224-9_58. [22] Fushiki, Tadayoshi. (2011). Estimation of prediction error by using K-fold cross-validation.

Statistics and Computing. 21. 137-146. 10.1007/s11222-009-9153-8. [23] Powers, David & Ailab,. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2. 2229-3981. 10.9735/2229-3981. L. F. Carvalho, G.

Fernandes, M. V. O. De Assis, J. J. P. C. Rodrigues, and

M. Lemes Proenc¸a, “Digital signature of network segment for healthcare environments support,” Irbm, vol. 35, no. 6, pp. 299-309, 2014. [24] A. Simons, “Using artificial intelligence to improve early breast cancer detection, “2017. Retrieved on

April 10, 2018, from https://www.csail.mit.edu/news/using-artificialintelligence-improveearly-breast-cancer-detection. [25] Medjahed, S.A., Saadi, T.A., & Benyettou, A. (2013).

Breast Cancer Diagnosis by using k-Nearest Neighbor