ISSN (Online) : 2456 - 0774

Email : ijasret@gmail.com

ISSN (Online) 2456 - 0774


Reducing Number of Parameters for Identifying Breast Cancer 


Abstract

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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Paper Submission Open For October 2024
UGC indexed in (Old UGC) 2017
Last date for paper submission 30th October, 2024
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Publication of Paper Within 15-30 Days after completing all the formalities
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Publication Fees  Rs.8000 (PG student)