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Neural Networks provide solutions to biologically inspired problem of medical domain like breast cancer. Neural Networks, Fuzzy Logic and Genetic Algorithms contribute novel algorithms to deal with breast cancer. Breast cancer can be diagnosed using soft computing methods. In this paper, we try to produce effective diagnosis of breast cancer by using feature reduction and classification methods. The net-effect of the classification before and after feature reduction process is stated. The feature reduction method applied is Principal Component Analysis (PCA) and the classification method includes Support Vector Machines (SVM). The result of the proposed method produced better outcome when applied on Breast Cancer Data Set .
Keywords:
Neural Networks, Breast Cancer, feature reduction, PCA, Classification, SVM
Cite Article:
"Diagnosis of breast cancer using Neural Networks", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.3, Issue 5, page no.163 - 166, May-2018, Available :http://www.ijrti.org/papers/IJRTI1805029.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator