Abstract

Breast cancer, the most common of types of cancer that threatens human life more specifically women can be diagnosed with classification techniques of data mining. This work is an extension of earlier implementation of breast cancer analysis of the author through iterative linear regressive classifier. The objective of this study is to make cent percent prediction accuracy in the diagnosis of breast cancer over the traditional Wisconsin dataset. The novelty of the paper includes the benefits of the previous ILRC and also takes the advantages of AI. The results of the proposed work are evaluated against the randmeasure and have proven that the results yield cent percent prediction accuracy in diagnosing breast cancer.

References

• Williams, Kehinde, Peter Adebayo Idowu, Jeremiah AdemolaBalogun, and AdeniranIsholaOluwaranti. "Breast cancer risk prediction using data mining classification techniques." Transactions on Networks and Communications 3, no. 2 ,pp: 01-11, 2015.
• Joy Christy, S. Hari Ganesh, “Building Numerical Clusters Using Multidimensional Spherical Equation”, International Journal of Applied Engineering Research, ISSN 0973-4562, Volume 10, Issue No.82, pp:629-634, 2015.
• RadhanathPatra and ShankhaMitraSunan, “A Review on Different Computing Method for Breast Cancer Diagnosis Using Artificial Neural Network and Data mining Techniques.” International Journal of Advanced Research, ISSN: 2320-5407, pp: 598-610, 2016.
• Venkatesan, E., and T. Velmurugan. "Performance analysis of decision tree algorithms for breast cancer classification." Indian Journal of Science and Technology 8.29 , pp:1-8, 2015.
• Majali, Jaimini, et al. "Data Mining Techniques For Diagnosis And Prognosis Of Cancer." International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 3, pp:613-616, 2015
• Sivakami, K. "Mining Big Data: Breast Cancer Prediction using DT-SVM Hybrid Model." International Journal of Scientific Engineering and Applied Science (IJSEAS) - Volume-1, Issue-5, pp:418-429, 2015
• Sumbaly, Ronak, N. Vishnusri, and S. Jeyalatha. "Diagnosis of Breast Cancer using Decision Tree Data Mining Technique." International Journal of Computer Applications 98.10, pp:16-24, 2014.
• Thein, HtetThazinTike, and Khin Mo Mo Tun. "An Approach for Breast Cancer Diagnosis Classification Using Neural Network." Advanced Computing 6.1, pp: 1-10, 2015.
• Samuel Giftson, A. Joy Christy, S. Hari Ganesh. “Novel Linear Regressive Classifier for the Diagnosis of Breast Cancer”, 2nd World Congress on Computing and Communication Technologies (WCCCT 2016), St. Joseph’s College, 2nd and 3rd February 2017.
• Christy, A. Joy, and S. Hari Ganesh. "Linear Regressive Percentage Split Distribution Clustering." International Journal of Control Theory an Applications, ISSN: 0974-5572, Impact Factor: 1.891, Indexed in Scopus.
• https://www.hiit.fi/u/ahonkela/dippa/node41.html
• http://radio.feld.cvut.cz/matlab/toolbox/nnet/hardlim.html
• https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/