Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

Research and Application of Data Mining in Chronic Diseases

Authors
Yuliang Shi, Jun Tao
Corresponding Author
Yuliang Shi
Available Online March 2018.
DOI
https://doi.org/10.2991/mecae-18.2018.39How to use a DOI?
Keywords
Apriori algorithm, data mining, association rules.
Abstract
In recent years, with the acceleration of people's pace of life, the number of chronic diseases in China is increasing. The attention and investment of the country to the medical industry is increasing year by year. At the same time, with the maturity and perfection of data mining technology, many countries have applied this technology to the research and mining of medical data. In this paper, the Apriori algorithm of data mining technology, and improve the data format of the Apriori algorithm is applied to the prediction of nephropathy, establish the association rules between chronic disease and a number of physical data by the algorithm, and the experimental results proved that Apriori algorithm is effective in the medical data mining.
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Proceedings
2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
Part of series
Advances in Engineering Research
Publication Date
March 2018
ISBN
978-94-6252-493-4
DOI
https://doi.org/10.2991/mecae-18.2018.39How 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  - Yuliang Shi
AU  - Jun Tao
PY  - 2018/03
DA  - 2018/03
TI  - Research and Application of Data Mining in Chronic Diseases
BT  - 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/mecae-18.2018.39
DO  - https://doi.org/10.2991/mecae-18.2018.39
ID  - Shi2018/03
ER  -