Predictive Analysis Of Breast Cancer Using Machine Learning Techniques

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Rashmi Agrawal

Abstract

This paper is a product of the research Project “Predictive Analysis Of Breast Cancer Using Machine Learning Techniques” performed in Manav Rachna International Institute of Research and Studies, Faridabad in the year 2018.


Introduction: The present article is part of the effort to predict breast cancer which is a serious concern for women’s health.


Problem: Breast cancer is the most common type of cancer and has always been a threat to women’s lives. Early diagnosis requires an effective method to predict cancer to allow physicians to distinguish benign and malicious cancer. Researchers and scientists have been trying hard to find innovative methods to predict cancer.


Objective: The objective of this paper will be predictive analysis of breast cancer using various machine learning techniques like Naïve Bayes method, Linear Discriminant Analysis, K-Nearest Neighbors and Support Vector Machine method. 


Methodology: Predictive data mining has become an instrument for scientists and researchers in the medical field. Predicting breast cancer at an early stage helps in better cure and treatment. KDD (Knowledge Discovery in Databases) is one of the most popular data mining methods used by medical researchers to identify the patterns and the relationship between variables and also helps in predicting the outcome of the disease based upon historical data of datasets.


Results: To select the best model for cancer prediction, accuracy of all models will be estimated and the best model will be selected.


Conclusion: This work seeks to predict the best technique with highest accuracy for breast cancer.


Originality: This research has been performed using R and the dataset taken from UCI machine learning repository.


Limitations: The lack of exact information provided by data.

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How to Cite
[1]
R. Agrawal, “Predictive Analysis Of Breast Cancer Using Machine Learning Techniques”, ing. Solidar, vol. 15, no. 29, pp. 1-23, Sep. 2019.
Section
Research Articles

References

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