Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

ISE: An Algorithm to Screen out the high-risk Group of Breast Cancer

Authors
Fei Chang, Rui Wang
Corresponding Author
Fei Chang
Available Online June 2017.
DOI
10.2991/caai-17.2017.66How to use a DOI?
Keywords
mobile and sever; data analysis; model of high -risk groups in breast cancer
Abstract

In the study for the prevention and the control of breast cancer, using mobile devices to design questionnaires and applying models to screen out high-risk groups has great signifi-cance. However, some existing models are not suitable for the Asian women. In this work, we proposed an ISE algorithm to derive the conditions of breast cancer by analyzing the relation-ship among the influential factors of breast cancer and the strength of the relationship. Based on this algorithm, we can obtain the high-risk groups of breast cancer and finally construct a model for the prevention and the control of breast cancer, especially for Asian women.

Copyright
© 2017, 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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Volume Title
Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-360-9
ISSN
1951-6851
DOI
10.2991/caai-17.2017.66How to use a DOI?
Copyright
© 2017, 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/).

Cite this article

TY  - CONF
AU  - Fei Chang
AU  - Rui Wang
PY  - 2017/06
DA  - 2017/06
TI  - ISE: An Algorithm to Screen out the high-risk Group of Breast Cancer
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 293
EP  - 295
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.66
DO  - 10.2991/caai-17.2017.66
ID  - Chang2017/06
ER  -