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Comparative study of machine learning techniques for breast cancer identification/diagnosis

Comparative study of machine learning techniques for breast cancer identification/diagnosis The number of new cases of female breast cancer was 124.9 per 100,000 women per year. Similarly, deaths were 21.2 per 100,000 women per year. It calls for an urge to increase the awareness of breast cancer and very accurately analyse the causes which may differ in minute variations. This is why the application of computation techniques are widely increasing to support the diagnostic results. In this paper, we present the application of several machine learning techniques and models like neural network, SVM is used to quantify the classifications. The techniques that are most reliable, accurate and robust are emphasised. It gives a plethora of explorations into the research field for developing predictive models. To achieve higher reliability on the data, we present the comparison of various Machine Learning techniques on a dataset that is available on the website Kaggle. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Enterprise Network Management Inderscience Publishers

Comparative study of machine learning techniques for breast cancer identification/diagnosis

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1748-1252
eISSN
1748-1260
DOI
10.1504/IJENM.2019.098102
Publisher site
See Article on Publisher Site

Abstract

The number of new cases of female breast cancer was 124.9 per 100,000 women per year. Similarly, deaths were 21.2 per 100,000 women per year. It calls for an urge to increase the awareness of breast cancer and very accurately analyse the causes which may differ in minute variations. This is why the application of computation techniques are widely increasing to support the diagnostic results. In this paper, we present the application of several machine learning techniques and models like neural network, SVM is used to quantify the classifications. The techniques that are most reliable, accurate and robust are emphasised. It gives a plethora of explorations into the research field for developing predictive models. To achieve higher reliability on the data, we present the comparison of various Machine Learning techniques on a dataset that is available on the website Kaggle.

Journal

International Journal of Enterprise Network ManagementInderscience Publishers

Published: Jan 1, 2019

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