Throughout the 20th century, views about breast cancer have drastically changed. Breast cancer is the most common cancer in women worldwide, with nearly 1.7 million new cases diagnosed in 2012. This type of cancer is the second most common cancer overall. Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine (SVM), Decision Tree (DT), K Nearest Neighbour (KNN), and Naïve Bayes (NB). This paper mostly focuses on detailed analysis and comparing the performance of above-mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, Precision, Misclassification Rate, False Positive Rate, True Positive Rate and Specificity. The main part of the project is creating a useful tool for predicting breast cancer with high accuracy before getting ill or in the initial stage of the disease. In other words, we can anticipate the future for women diseases.

Machine Learning, Breast Cancer, CART, Naive Bayes, K nearest neighbors, Support Vector Machine

[1] Cortes C, Vapnik V. “Support-vector Networks”. Machine Learning, 20(3):pp. 273-297. 1995.
[2] P. Manoj Kumar, R. Sugumaran and D. Zerr, "A rule-based classifier using Classification and Regression Tree (CART) approach for urban landscape dynamics," IEEE International Geoscience and Remote Sensing Symposium, Toronto, Ontario, Canada, Vol.2, pp. 1193-1194, 2002
[3] A. M. Amiri and G. Armano, "Early diagnosis of heart disease using classification and regression trees," The 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, 2013, pp.1-4. doi: 10.1109/IJCNN.2013.6707080
[4] A. F. A. Pinem and E. B. Setiawan, "Implementation of classification and regression Tree (CART) and fuzzy logic algorithm for intrusion detection system," 2015 3rd International Conference on Information and Communication Technology (ICoICT), Nusa Dua, pp. 266-271, 2015.
[5] H. R. Bittencourt and R. T. Clarke, "Use of classification and regression trees (CART) to classify remotely-sensed digital images," IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), Toulouse, Vol.6, pp. 3751-3753. 2006 doi: 10.1109/IGARSS.2003.1295258
[6] N. Bhargava, R. Purohit, S. Sharma and A. Kumar, "Prediction of arthritis using classification and regression tree algorithm," 2017 2nd International Conference on Communication and Electronics Systems (ICCES), Coimbatore,pp. 606-610. 2017.
[7] Xuegong, Zhang. "Introduction to statistical learning theory and support vector machines". Acta Automatica Sinica 26.1 (2000): pp.32-42.
[8] Weinberger, Kilian Q., John Blitzer, and Lawrence Saul. "Distance metric learning for large margin nearest neighbor classification". Advances in neural information processing systems 18(2006): pp.1473
[9] Nigel Williams and Sebastian Zander and Grenville J. Armitage. “A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification”. Computer Communication Review (2006).volume=36:pp.5-16.
[10] S. R. Safavian and D. Landgrebe, "A survey of decision tree classifier methodology," in IEEE Transactions on Systems, Man, and Cybernetics, vol. 21, no. 3, pp. 660-674, May-June 1991.
doi: 10.1109/21.97458
[11] Watson, Thomas J. . “An empirical study of the naive Bayes classifier.” (2001).
[12] Y. Huang and L. Li, "Naive Bayes classification algorithm based on small sample set," 2011 IEEE International Conference on Cloud Computing and Intelligence Systems, Beijing, pp. 34-39. 2011 . doi: 10.1109/CCIS.2011.6045027
[13] Sanjay S Bhadoria and Rajendra Kumar Patel, “Web Text Content Extraction and Classification using Naïve Bayes Classifier Algorithm”, International Journal of Scientific Research in Computer Science and Engineering, Vol.2, Issue.5, pp.1-4, 2014
[14] Sona Taheri and Musa Mammadov. 2013. “Learning the naive Bayes classifier with optimization models”. Int. J. Appl. Math. Comput. Sci. 23, Vol.4 pp.787-795. (December 2013), DOI: https://doi.org/10.2478/amcs-2013-0059
[15] Zheng F., Webb G.I. (2011) “Tree Augmented Naive Bayes”. In: Sammut C., Webb G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. 2011.
[16] (2011) C4.5. In: Sammut C., Webb G.I. (eds). “Encyclopedia of Machine Learning”. Springer, Boston, MA
[17] Jiang, Liangxiao et al. “A Novel Bayes Model: Hidden Naive Bayes.” IEEE Transactions on Knowledge and Data Engineering 21 ,pp.1361-1371. 2009
[18] Y. Ji, S. Yu and Y. Zhang, "A novel Naive Bayes model: Packaged Hidden Naive Bayes," 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, Chongqing, pp. 484-487. 2011. doi: 10.1109/ITAIC.2011.6030379
[19] Y. Wu, "A New Instance-weighting Naive Bayes Text Classifiers," 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), Lanzhou, pp. 198-202. 2018. doi: 10.1109/IRCE.2018.8492960
[20] Imandoust, S.B. & Bolandraftar, Mohammad. (2013). “Application of K-nearest neighbor (KNN) approach for predicting economic events theoretical background”. Int J Eng Res Appl. 3.pp. 605-610. 2013.
[21] Guo G., Wang H., Bell D., Bi Y., Greer K. (2003) “KNN Model-Based Approach in Classification”. In: Meersman R., Tari Z., Schmidt D.C. (eds) On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. OTM. Lecture Notes in Computer Science, vol. 2888. Springer, Berlin, Heidelberg. 2003.
[22] S. Sun and R. Huang, "An adaptive k-nearest neighbor algorithm," 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, Yantai, pp. 91-94.
2010. doi: 10.1109/FSKD.2010.5569740
[23] Okfalisa, I. Gazalba, Mustakim and N. G. I. Reza, "Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification," 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, pp. 294-298. 2017. doi: 10.1109/ICITISEE.2017.8285514
[24] P. Surlakar, S. Araujo and K. M. Sundaram, "Comparative Analysis of K-Means and K-Nearest Neighbor Image Segmentation Techniques," 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, 2016, pp. 96-100.
[25] K. K. Paliwal and P. V. S. Rao, "Application of k-Nearest-Neighbor Decision Rule in Vowel Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-5, no. 2, pp. 229-231, March 1983. doi: 10.1109/TPAMI.1983.4767378
[26] B. Wang, Yong Zeng and Yupu Yang, "Generalized nearest neighbor rule for pattern classification," 2008 7th World Congress on Intelligent Control and Automation, Chongqing, pp. 8465-8470. 2008.doi: 10.1109/WCICA.2008.4594258
[27] Gangadeep Kaur, Kamaldeep Kaur, “Sentiment Detection from Panjabi Text using Support Vector Machine”, International Journal of Scientific Research in Computer Science and Engineering, Vol.2, Issue.6, pp.39-46, 2017 .
[28] Ghattas, Badih & Ishak, Anis. (2019). “An Efficient Method for Variables Selection Using SVM-Based Criteria”. Journal of Machine Learning Research - JMLR. 2019.
[29] B. Waske and J. A. Benediktsson, "Fusion of Support Vector Machines for Classification of Multisensor Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 12, pp. 3858-3866, Dec. 2007.
doi: 10.1109/TGRS.2007.898446
[30] G. Zhang, "A Modified SVM Classifier Based on RS in Medical Disease Prediction," 2009 Second International Symposium on Computational Intelligence and Design, Changsha, pp. 144-147. 2009. doi: 10.1109/ISCID.2009.43
[31] .M. Sewak, P. Vaidya, C. Chan and Zhong-Hui Duan, "SVM Approach to Breast Cancer Classification," Second International Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2007), Iowa City, IA, pp.32-37. 2017 .
[32] M. Ma, Z. Gao, J. Wu, Y. Chen and X. Zheng, "A Smile Detection Method Based on Improved LeNet-5 and Support Vector Machine," 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Guangzhou,pp.446-451.2018.
[33] M. H. Selamat and H. M. Rais, "Enhancement on image face recognition using Hybrid Multiclass SVM (HM-SVM)," 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), Kuala Lumpur, pp. 424-429. 2016. doi: 10.1109/ICCOINS.2016.7783253
[34] Parveen and A. Singh, "Detection of brain tumor in MRI images, using combination of fuzzy c-means and SVM," 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, pp. 98-102. 2015 doi: 10.1109/SPIN.2015.7095308
[35] B. Sanjaa and E. Chuluun, "Malware detection using linear SVM," Ifost, Ulaanbaatar, pp. 136-138. 2013 doi: 10.1109/IFOST.2013.6616872
[36] C. Wang, J. Zheng and X. Li, "Research on DDoS Attacks Detection Based on RDF-SVM," 2017 10th International Conference on Intelligent Computation Technology and Automation (ICICTA), Changsha, pp. 161-165. 2017 .doi: 10.1109/ICICTA.2017.43