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Author:

Shi, Yuliang (Shi, Yuliang.) | Tao, Jun (Tao, Jun.)

Indexed by:

CPCI-S

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.

Keyword:

data mining Apriori algorithm association rules

Author Community:

  • [ 1 ] [Shi, Yuliang]Sch Beijing Univ Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Tao, Jun]Sch Beijing Univ Technol, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Shi, Yuliang]Sch Beijing Univ Technol, Beijing 100124, Peoples R China

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Source :

PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON MECHANICAL, ELECTRONIC, CONTROL AND AUTOMATION ENGINEERING (MECAE 2018)

ISSN: 2352-5401

Year: 2018

Volume: 149

Page: 186-190

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

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