Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6): 210-214.doi: 10.11896/jsjkx.200500082

• Artificial Intelligence • Previous Articles     Next Articles

Relative Risk Degree Based Risk Factor Analysis Algorithm for Congenital Heart Disease in Children

XU Hui-hui, YAN Hua   

  1. School of Computer Science & Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
  • Received:2020-05-19 Revised:2020-08-08 Online:2021-06-15 Published:2021-06-03
  • About author:XU Hui-hui,born in 1995,postgra-duate.Her main research interests include data mining and so on.([email protected])
    YAN Hua,born in 1970,Ph.D,associate professor.Her main research interests include computational intelligence and data mining.
  • Supported by:
    National Natural Science Foundation of China(61976046) and Key Research and Development Projects of Sichuan Province(2018SZ0065).

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Abstract: The analysis of disease-related risk factors is an important part of application of data mining theory in the medical field,which is helpful for doctors to analyze causes of disease and carry out effective work of disease prevention and control.But disease data in the medical field have their own characteristics,such as high imbalance,which means that most valuable information is contained in the attribute items with a small support.It is easy to lose important information when applying the classical association rule algorithm based on the support directly.Therefore,based on the knowledge of medical field and the common statistical standard of medical field——Relative Risk,this paper proposes a mining algorithm for high relative risk itemsets(MARRI) and two corresponding pruning methods,which are interaction pruning and sample number pruning,and verifies the algorithm on the dataset of children’s congenital heart disease.Experimental results show that the algorithm is effective to mine the information in low support items and disease-related factors mined out are more valuable.

Key words: Association rules, Data mining, Disease analysis, Relative risk

CLC Number: 

  • TP181
[1]AGRAWAL R,IMIELIŃSKI T,SWAMI A.Mining association rules between sets of items in large database[J].ACM SIGMOD Record,1993,22(2):207-216.
[2]GAO L,WANG J,LI F G,et al.Symptoms-herbs relationship in lung diseases based on association rules[J].Journal of Traditional Chinese Medicine,2013,54(8):697-700.
[3]JIANG Y R,XIE Y H,ZHANG J C,et al.Data mining of the medication rule of Chen Keji in the treatment of blood stasis syndrome of cardiovascular disease[J].Journal of Traditional Chinese Medicine,2015,56(5):376-380.
[4]LI Q,CHEN D T,LUO X L.Implementation of the association rule algorithm in medical big data[J].Software Engineering,219,22(1):12-15.
[5]LEE W H,WANG E T,CHEN A L P.Mining accompanying relationships between diseases from patient records[C]//IEEE International Conference on Big Data.IEEE,2018:3861-3868.
[6]WANG M X.The prediction model for disease based on logistic regression and association rules[D].Jinan:Shandong Univer-sity,2016.
[7]GAO S Y,CHENG S Z.Application of clustering-based entropy weighted association analysis[J/OL].[2019-01].http://dpi-proceedings.com/index.php/dtcse/article/view/27565.
[8]OJHA D,PANDEY P.Optimizing Association Rule using Ge-netic Algorithm and Data Sampling Approach[J].International Journal of Computer Applications,2018,179(11):15-19.
[9]DING Y,ZHU C S,WU Y Y.Association Rule Mining Algo-rithm Based on Hadoop[J].Computer Science,2018,45(11A):409-411,416.
[10]IBRAHIM A,SHEHADA D.Study of Association Rule Mining for Discovery of Frequent Item Sets on Big Data Sets[J].International Journal of Materials Science,2018,13(4):345-358.
[11]LIU Z,HU L,WU C,et al.A novel process-based association rule approach through maximal frequent itemsets for big data processing[J].Future Generation Computer Systems,2018,81:414-424.
[12]WANG Q S,JIANG F S,LI F.Multi-label Learning Algorithm Based on Association Rules in Big Data Environment[J].Computer Science,2020,47(5):90-95.
[13]RATHEE S,KASHYAP A.Adaptive-Miner:an efficient dis-tributed association rule mining algorithm on Spark[J].Journal of Big Data,2018,5(1):6.
[14]VOUGAS K,KROCHMAL M,JACKSON T,et al.Deep lear-ning and association rule mining for predicting drug response in cancer[J/OL].https://www.biorxiv.org/content/10.1101/070490v3.full.
[15]KHAN A,USMAN M.Early diagnosis of Alzheimer’s disease using machine learning techniques:a review paper[C]//2015 7th International Joint Conference on Knowledge Discovery.IEEE,2016:380-387.
[16]ZHOU W,NIELSEN J B,FRITSCHE L G,et al.Efficientlycontrolling for case-control imbalance and sample relatedness in large-scale genetic association studies[J].Nature Genetic,2018,50(9):1335-1341.
[17]WANG W P.A dissertation for the master degree of engineering[D].Zhangzhou:Minnan Normal University,2016.
[18]CUI X J.The study of association rule based classification forimbalanced data[D].Dalian:Dalian University of Technology,2015.
[19]LI M L.Epidemiology[M].Beijing:People’s Medical Publishing House,2008:71.
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