Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/inderscience-publishers/comparative-study-of-machine-learning-techniques-for-breast-cancer-4MHWV9Z0gn
References
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
- 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 Management – Inderscience Publishers
Published: Jan 1, 2019