Skip to main content
SpringerLink
Log in

Medical Insurance Data Mining Using SPAM Algorithm

  • Conference paper
  • First Online:
Book cover Geo-Spatial Knowledge and Intelligence (GRMSE 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 698))

Abstract

The sequential pattern data mining technology is widely applied to various fields, and it brings an indispensable value for many areas, especially in the field of medical treatment. But the amount of health-care data is large and the information included is extensive, so some valuable information may have not been found, which needs us to take the further research. By using the Sequential Pattern Mining (SPAM) algorithm to deal with the health-care data, we try to find the user’s medical behavior and the doctor diagnosis model or rule. This article first introduces the characteristics and the worth of Medicare data and data mining on it, especially the sequence pattern mining significance. Then discusses the ideas and characteristics of SPAM algorithm and the advantages of high efficiency, we use SPAM algorithm to deal with health care data, and try to find the regularity of visiting doctor, medical treatment characteristics and drug-use mode about insured person in a certain period of time. The experiments reveal the treatment mode and characteristics of the drug of pregnancy, which can provide guidance and reference for the diagnosis and treatment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Feng, L.: Review of data mining application in medical insurance in our country. Comput. Knowl. Technol. 21, 880–882 (2014)

    Google Scholar 

  2. Du, Y.: Studying on the design of overall basic medical insurance system. Chin. Health Serv. Manag. 32, 90–92 (2015)

    Google Scholar 

  3. Zhao, Y.: Mechanism of prepaid method of medical insurance in controlling medical expense. Chin. Hosp. Manag. 35, 45–47 (2015)

    Google Scholar 

  4. Lei, Y., Li, M., Hu, W., Song, G.: Efficient methods for rare sequential pattern mining. J. Frontiers Comput. Sci. Technol. 9, 429–437 (2015)

    Google Scholar 

  5. Xu, Y.: Data preparation for risk control of medical insurance fund. Fudan University, Jilin (2010)

    Google Scholar 

  6. Liu, S., Yang, H.: Study on medical insurance fraud detection system based on multi-agent. Comput. Technol. Dev. 23, 171–174 (2013)

    Google Scholar 

  7. Qu, G., Cui, S., Tang, J.: Active and passive factors analysis of the medical insurance fund expenditure–taking Dalian as an example. vol. 19, pp. 120–125 (2014)

    Google Scholar 

  8. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th International Conference on Data Engineering. IEEE Computer Society, Los Alamitos (1995)

    Google Scholar 

  9. Tao, H., Jiang, F.: Application of improved sequential pattern mining in hospital referral. Comput. Syst. Appl. 10, 253–258 (2015)

    Google Scholar 

  10. Xu, T.T., Dong, X.J.: E-MsNFIS: efficient negative frequent itemsets mining based on multiple minimum supports. Appl. Mech. Mater. 411–414, 386–389 (2013)

    Article  Google Scholar 

  11. Cao, L., Dong, X., Zheng, Z.: e-NSP: efficient negative sequential pattern mining. Artif. Intell. 235, 156–182 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  12. Xiangjun, D.: Mining infrequent itemsets based on extended MMS Model. Commun. Comput. Inf. Sci. 2, 190–198 (2007)

    Google Scholar 

  13. Imran, M.: Authentication user’s privacy: an integrating location privacy protection algorithm for secure moving objects in location based services. Wirel. Pers. Commun. 82, 1585–1600 (2015)

    Article  Google Scholar 

  14. Memon, I., Chen, L., Majid, A., Lv, M., Hussain, I., Chen, G.: Travel recommendation using geo-tagged photos in social media for tourist. Wirel. Pers. Commun. 80, 1347–1362 (2015)

    Article  Google Scholar 

  15. Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Proceedings of the 5th International Conference Extending Database Technology, EDBT 1996, London, UK (1996)

    Google Scholar 

  16. Cheng, S., Ma, C., Li, C.: High utility sequential pattern mining algorithm based on MapReduce. Comput. Syst. Appl. 12, 228–232 (2015)

    Google Scholar 

  17. Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proceeding of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, New York, USA, pp. 429–435 (2002)

    Google Scholar 

  18. Chen, J., Weng, Z.: An algorithm of SPAM based on projected database. Comput. Knowl. Technol. 6, 1537–1539 (2010)

    Google Scholar 

  19. Zhang, W., Liu, F., Teng, S.: Improved PrefixSpan algorithm and its application in sequential pattern mining. J. Guangdong Univ. Technol. 30, 49–54 (2013)

    Google Scholar 

Download references

Acknowledgments

This work was supported partly by the Jinan youth science and technology star plan (No. 20120104), National Natural Science Foundation of China (71271125) and Scientific Research Development Plan Project of Shandong Provincial Education Department (J12LN10).

Author information

Authors and Affiliations

  1. Qilu University of Technology, Jinan, 25000, China

    Qifeng Cheng & Xiaoqiang Ren

Authors
  1. Qifeng Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoqiang Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoqiang Ren .

Editor information

Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Hanning Yuan

  2. Beijing Institute of Technology, Beijing, China

    Jing Geng

  3. Wuhan University, Wuhan, China

    Fuling Bian

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Cheng, Q., Ren, X. (2017). Medical Insurance Data Mining Using SPAM Algorithm. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-10-3966-9_11

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3966-9_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3965-2

  • Online ISBN: 978-981-10-3966-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation