Mining association rules between stroke risk factors based on the Apriori algorithm

被引：22
|
作者：
Li, Qin [1 ]
Zhang, Yiyan [1 ]
Kang, Hongyu [1 ]
Xin, Yi [1 ]
Shi, Caicheng [1 ]
机构：
[1] Beijing Inst Technol, Biomed Engn Dept, 5 South Zhongguancun St, Beijing 100081, Peoples R China
来源：
TECHNOLOGY AND HEALTH CARE | 2017年 / 25卷
关键词：
Apriori algorithm; association rules; stroke; risk factors; ATRIAL-FIBRILLATION;
D O I：
10.3233/THC-171322
中图分类号：
R19 [保健组织与事业（卫生事业管理）];
学科分类号：
摘要：
BACKGROUND: Stroke is a frequently-occurring disease and is a severe threat to human health. OBJECTIVE: We aimed to explore the associations between stroke risk factors. METHODS: Subjects who were aged 40 or above were requested to do surveys with a unified questionnaire as well as laboratory examinations. The Apriori algorithm was applied to find out the meaningful association rules. Selected association rules were divided into 8 groups by the number of former items. The rules with higher confidence degree in every group were viewed as the meaningful rules. RESULTS: The training set used in association analysis consists of a total of 985,325 samples, with 15,835 stroke patients (1.65%) and 941,490 without stroke (98.35%). Based on the threshold we set for the Apriori algorithm, eight meaningful association rules were obtained between stroke and its high risk factors. While between high risk factors, there are 25 meaningful association rules. CONCLUSIONS: Based on the Apriori algorithm, meaningful association rules between the high risk factors of stroke were found, proving a feasible way to reduce the risk of stroke with early intervention.
引用
收藏
页码：S197 / S205
页数：9
相关论文
共 50 条
  • [21] Database encoding and an anti-Apriori algorithm for association rules mining
    Wang, Tong
    He, Pi-Lian
    PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2006, : 1195 - +
  • [22] Two revised algorithms based on Apriori for mining association rules
    Ma, Wei-Min
    Liu, Zhu-Ping
    PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2008, : 350 - +
  • [23] Improvement of Apriori Algorithm for Association Rules
    Li, Xiaohui
    2011 INTERNATIONAL CONFERENCE ON FUTURE COMPUTER SCIENCE AND APPLICATION (FCSA 2011), VOL 4, 2011, : 312 - 315
  • [24] Association rules mining algorithm using sharing mechanism niche leaping ant colony based on apriori algorithm
    Wang, Yang
    Xu, Yakun
    Li, Gang
    Wang, Hongliang
    Journal of Computational and Theoretical Nanoscience, 2015, 12 (10) : 3223 - 3231
  • [25] Improvement of Apriori Algorithm for Association Rules
    Li Xiaohui
    2012 WORLD AUTOMATION CONGRESS (WAC), 2012,
  • [26] An improved apriori algorithm for association rules
    Department of Computer and Information Engineering, Heilongjiang Institute of Science and Technology, Harbin 150027, Heilongjiang, China
    Liu, X. (liuxingli_usth@139.com), 1600, Universitas Ahmad Dahlan, Jalan Kapas 9, Semaki, Umbul Harjo,, Yogiakarta, 55165, Indonesia (11):
  • [27] MR-Apriori: Association Rules Algorithm Based on MapReduce
    Lin, Xueyan
    2014 5TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS), 2014, : 141 - 144
  • [28] Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues for Human Participatory Based on Association Rules
    Li, Zhi
    Li, Xuyu
    Tang, Runhua
    Zhang, Lin
    FRONTIERS IN PSYCHOLOGY, 2021, 11
  • [29] An Approach to Improve Apriori Algorithm Based On Association rule Mining
    Yadav, Chanchal
    Wang, Shuliang
    Kumar, Manoj
    2013 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATIONS AND NETWORKING TECHNOLOGIES (ICCCNT), 2013,
  • [30] A multi-GPU implementation of apriori algorithm for mining association rules in medical data
    School of Computer Science, Dalian University of Technology, No 2 Linggong Road, Dalian, China
    不详
    ICIC Express Lett., 5 (1303-1310):
12345