Breast Cancer Risk Diagnosis based on Random Forest Classification

DOI

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.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention
PB  - Atlantis Press
SP  - 446
EP  - 452
SN  - 1951-6851
UR  - https://doi.org/10.2991/rac-16.2016.72
DO  - 10.2991/rac-16.2016.72
ID  - Li2016/11
ER  -