Research on Correlation Model of Diabetes Complications Based on CI-Apriori Algorithm

Yanan Guo; Shuli Guo; Lina Han

doi:10.1109/CCDC58219.2023.10326707

Research on Correlation Model of Diabetes Complications Based on CI-Apriori Algorithm

Yanan Guo
, Shuli Guo
, Lina Han^*

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology
General Hospital of People's Liberation Army

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In recent years, the rapid development of computer technology has made great progress in the field of smart health-care. Using data mining technology can obtain useful information in medical big data, discover the correlations among diseases and achieve the prevention and control of diseases. However, the traditional mining algorithms can no longer satisfy the demands of medical big data, it is an important direction of future research to improve and optimize the algorithms to make them applicable to the medical field. Based on this, this paper improves Apriori algorithm through parallel processing, matrix compression, and the introduction of lifting rate and interest. The optimized algorithm is called CI-Apriori, and an association rule model of diabetes complications based on CI-Apriori is proposed. The experimental results show that CI-Apriori has a great improvement in time and space efficiency, and can mine the strong association rules among diabetes complications faster and more effectively, so as to find the medical laws for the prevention and treatment of diseases.

Original language	English
Title of host publication	Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3789-3794
Number of pages	6
ISBN (Electronic)	9798350334722
DOIs	https://doi.org/10.1109/CCDC58219.2023.10326707
Publication status	Published - 2023
Event	35th Chinese Control and Decision Conference, CCDC 2023 - Yichang, China Duration: 20 May 2023 → 22 May 2023

Publication series

Name	Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

Conference

Conference	35th Chinese Control and Decision Conference, CCDC 2023
Country/Territory	China
City	Yichang
Period	20/05/23 → 22/05/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

CI-Apriori algorithm
association rule model
data mining
diabetes complications

Access to Document

10.1109/CCDC58219.2023.10326707

Cite this

Guo, Y., Guo, S., & Han, L. (2023). Research on Correlation Model of Diabetes Complications Based on CI-Apriori Algorithm. In Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023 (pp. 3789-3794). (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC58219.2023.10326707

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title = "Research on Correlation Model of Diabetes Complications Based on CI-Apriori Algorithm",

abstract = "In recent years, the rapid development of computer technology has made great progress in the field of smart health-care. Using data mining technology can obtain useful information in medical big data, discover the correlations among diseases and achieve the prevention and control of diseases. However, the traditional mining algorithms can no longer satisfy the demands of medical big data, it is an important direction of future research to improve and optimize the algorithms to make them applicable to the medical field. Based on this, this paper improves Apriori algorithm through parallel processing, matrix compression, and the introduction of lifting rate and interest. The optimized algorithm is called CI-Apriori, and an association rule model of diabetes complications based on CI-Apriori is proposed. The experimental results show that CI-Apriori has a great improvement in time and space efficiency, and can mine the strong association rules among diabetes complications faster and more effectively, so as to find the medical laws for the prevention and treatment of diseases.",

keywords = "CI-Apriori algorithm, association rule model, data mining, diabetes complications",

author = "Yanan Guo and Shuli Guo and Lina Han",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 35th Chinese Control and Decision Conference, CCDC 2023 ; Conference date: 20-05-2023 Through 22-05-2023",

year = "2023",

doi = "10.1109/CCDC58219.2023.10326707",

language = "English",

series = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3789--3794",

booktitle = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",

address = "United States",

}

Guo, Y, Guo, S & Han, L 2023, Research on Correlation Model of Diabetes Complications Based on CI-Apriori Algorithm. in Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 3789-3794, 35th Chinese Control and Decision Conference, CCDC 2023, Yichang, China, 20/05/23. https://doi.org/10.1109/CCDC58219.2023.10326707

Research on Correlation Model of Diabetes Complications Based on CI-Apriori Algorithm. / Guo, Yanan; Guo, Shuli; Han, Lina.
Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 3789-3794 (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research on Correlation Model of Diabetes Complications Based on CI-Apriori Algorithm

AU - Guo, Yanan

AU - Guo, Shuli

AU - Han, Lina

PY - 2023

Y1 - 2023

N2 - In recent years, the rapid development of computer technology has made great progress in the field of smart health-care. Using data mining technology can obtain useful information in medical big data, discover the correlations among diseases and achieve the prevention and control of diseases. However, the traditional mining algorithms can no longer satisfy the demands of medical big data, it is an important direction of future research to improve and optimize the algorithms to make them applicable to the medical field. Based on this, this paper improves Apriori algorithm through parallel processing, matrix compression, and the introduction of lifting rate and interest. The optimized algorithm is called CI-Apriori, and an association rule model of diabetes complications based on CI-Apriori is proposed. The experimental results show that CI-Apriori has a great improvement in time and space efficiency, and can mine the strong association rules among diabetes complications faster and more effectively, so as to find the medical laws for the prevention and treatment of diseases.

AB - In recent years, the rapid development of computer technology has made great progress in the field of smart health-care. Using data mining technology can obtain useful information in medical big data, discover the correlations among diseases and achieve the prevention and control of diseases. However, the traditional mining algorithms can no longer satisfy the demands of medical big data, it is an important direction of future research to improve and optimize the algorithms to make them applicable to the medical field. Based on this, this paper improves Apriori algorithm through parallel processing, matrix compression, and the introduction of lifting rate and interest. The optimized algorithm is called CI-Apriori, and an association rule model of diabetes complications based on CI-Apriori is proposed. The experimental results show that CI-Apriori has a great improvement in time and space efficiency, and can mine the strong association rules among diabetes complications faster and more effectively, so as to find the medical laws for the prevention and treatment of diseases.

KW - CI-Apriori algorithm

KW - association rule model

KW - data mining

KW - diabetes complications

UR - https://www.scopus.com/pages/publications/85181832326

U2 - 10.1109/CCDC58219.2023.10326707

DO - 10.1109/CCDC58219.2023.10326707

M3 - Conference contribution

AN - SCOPUS:85181832326

T3 - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

SP - 3789

EP - 3794

BT - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 35th Chinese Control and Decision Conference, CCDC 2023

Y2 - 20 May 2023 through 22 May 2023

ER -

Guo Y, Guo S, Han L. Research on Correlation Model of Diabetes Complications Based on CI-Apriori Algorithm. In Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 3789-3794. (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023). doi: 10.1109/CCDC58219.2023.10326707

Research on Correlation Model of Diabetes Complications Based on CI-Apriori Algorithm

Abstract

Publication series

Conference

UN SDGs

Keywords

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