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A Novel Iterative Linear Regression Perceptron Classifier for Breast Cancer Prediction

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Samuel Giftson Durai, S. Hari Ganesh

Samuel Giftson Durai and Hari S Ganesh. A Novel Iterative Linear Regression Perceptron Classifier for Breast Cancer Prediction. International Journal of Computer Applications 167(12):9-17, June 2017. BibTeX

	author = {Samuel Giftson Durai and S. Hari Ganesh},
	title = {A Novel Iterative Linear Regression Perceptron Classifier for Breast Cancer Prediction},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {12},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {9-17},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2017914461},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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Regression, perceptron, classification, data mining, linear functions