ISSN (Online) : 2456 - 0774

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ISSN (Online) 2456 - 0774

Reducing Number of Parameters for Identifying Breast Cancer 


ABSTRACT: Cancer is the major problemtoday, breast cancer is one of the leading cause of the death among the women.Here we are proposing a system which will be used by the medical experts, usingdata analytics techniques. The proposed system reduces the number of parametersof the fine needle aspiration test. Here we are using R programming languagefor the statistical analysis purpose. The proposed system consists of thesemajor steps: the data set is loaded into the main memory, then it ispre-processed by using PCA (i.e. Principle Component Analysis) algorithm, thendata is analysed and finally machine learning algorithms (LDA, RF model) areapplied. There are total 30 number of parameters in the fine needle aspirationtest, and we successfully reduce it to 20.finally the result is displayed usingvisual analytics tool.

Keyword: - Principalcomponent analysis (PCA), Linear Discriminant Analysis (LDA),Random

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Issue Publication   On 30 th October 2019