Proceedings of the 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention

Breast Cancer Risk Diagnosis based on Random Forest Classification

Authors
Li Li, Yuting Sun, Lei Xiao
Corresponding Author
Li Li
Available Online November 2016.
DOI
https://doi.org/10.2991/rac-16.2016.72How to use a DOI?
Keywords
Random Forest Classification; Breast Cancer; OOB Estimation
Abstract
In view of the good generalization performance of the random forest classifier, this paper uses the random forest classifier to analyze the risk of the 961 groups of breast tumor lesion tissue digital mammography image data. Empirical results show that the random forest classifier has better generalization performance than Decision Tree, Support Vector Machine and Recent Neighbor Method, and breast tumor severity of influential variable importance is as follows: Margin, Shape, Age and Density.
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Proceedings
7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC-2016)
Publication Date
November 2016
ISBN
978-94-6252-242-8
DOI
https://doi.org/10.2991/rac-16.2016.72How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Li Li
AU  - Yuting Sun
AU  - Lei Xiao
PY  - 2016/11
DA  - 2016/11
TI  - Breast Cancer Risk Diagnosis based on Random Forest Classification
BT  - 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC-2016)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/rac-16.2016.72
DO  - https://doi.org/10.2991/rac-16.2016.72
ID  - Li2016/11
ER  -