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Identifying the Association Rule to Determine the Possibilities of Cardio Vascular Diseases (CVD)

  • Avijit Kumar Chaudhuri
  • Anirban Das
  • Madhumita AddyEmail author
Conference paper
  • 60 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

depending on the insured’s age, life insurance companies set the premiums. there are age-slabs for which premiums are set and after a proper medical examination, after a certain age, life insurance is issued. major insurance company in india such as india life insurance corporation limited is seeking medical screening for any applicant above 45 years of age. candidates whose health is not commensurate with age have been observed. this is particularly true of cardiovascular diseases (cvd). therefore, the same can be tailored for individual candidates based on their medical test history instead of premiums based on age slabs. checking for cvd, however, requires a number of medical tests, prompting both the applicant and insurance companies to use this method. this can be streamlined by conducting only main medical tests to determine the cardio-vascular system status of the applicant. the paper outlines the primary tests needed to be conducted to assess a person seeking health insurance’s risk of cardiovascular disease. a series of association rules are developed to classify the risk of cardiovascular disease, using three well-proven methods. the three methods are clustering of k means, decision tree and logistics regression. this study suggests that premiums for health insurance should be based on the results of main assessments and their analysis of association rules. the combination of the three methods minimizes the type 1 error.

Keywords

Cardio vascular diseases (CVD) CART Logistic regression K-means algorithm Decision trees 

References

  1. 1.
    Crockett, D., Eliason, B.: What is DM in Healthcare. Health Catalyst (2017)
  2. 2.
    Cunningham, P.J., Ginsburg, P.B.: What accounts for differences in uninsurance rates across communities. J. Health Care Org. Provision Fin. 38(1), 6–21 (2001)
  3. 3.
    Glover, S., Moore, C.G., Probst, J.C., Samuels, M.E.: Disparities in access to care among rural working-age adults. J. Rural Health 20(3), 193–205 (2004)
  4. 4.
    Wu, C.H., Kao, S.C., Su, Y.Y., Wu, C.C.: Targeting customers via discovery knowledge for the insurance industry. Exp. Syst. Appl. 29(2), 291–299 (2005)
  5. 5.
    Monheit, A.C., Vistnes, J.P.: Race/ethnicity and health insurance status: 1987 and 1996. Med. Care Res. Rev. 57, 11–35 (2000)
  6. 6.
    US Government Printing Office.: Healthy People 2010 Understanding and Improving Health. In: SDoHaHS (ed.) Office of Disease Prevention and Health Promotion, 2nd edn. (2000)
  7. 7.
    US Government Printing Office.: Healthy People 2010 Understanding and Improving Health. USDoHaHS (ed.) Office of Disease Prevention and Health Promotion, 2nd edn. (2000)
  8. 8.
    Herring, B.: The effect of the availability of charity care to the uninsured on the demand for private health insurance. J. Health Econ. 24(2), 225–252 (2005)
  9. 9.
    Jonk, Y.C., Call, K.T., Cutting, A.H., O’Connor, H., Bansiya, V., Harrison, K.: Health care coverage and access to care—the status of Minnesota’s Veterans. Med. Care 43(8), 769–774 (2005)
  10. 10.
    Hendryx, M.S., Ahern, M.M., Lovrich, N.P., McCurdy, A.H.: Access to health care and community social capital. Health Serv. Res. 37(1), 87–103 (2002)
  11. 11.
    Carrasquillo, O., Himmelstein, D.U., Woolhandler, S., Bor, D.H.: Going bare: trends in health insurance coverage, 1989 through 1996. Am. J. Pub. Health 89(1), 36–42 (1999)
  12. 12.
    Carrasquillo, O., Carrasquillo, A.I., Shea, S.: Health insurance coverage of immigrants living in the United States: differences by citizenship status and country of origin. Am. J. Pub. Health 90(6), 917–923 (2000)
  13. 13.
    Shi, L.Y.: Vulnerable populations and health insurance. Med. Care Res. Rev. 57(1), 110–134 (2000)
  14. 14.
    Cardon, J.H., Hendel, I.: Asymmetric information in health insurance: evidence from the National Medical Expenditure Survey. Rand J. Econ. 32(3), 408–427 (2001)
  15. 15.
    Woolhandler, S., Himmelstein, D.U., Distajo, R., Lasser, K.E., McCormick, D., Bor, D.H., et al.: America’s neglected veterans: 1.7 million who served have no health coverage. Int. J. Health Serv. 35(2), 313–323 (2005)
  16. 16.
    Uysal, M., Roubi, M.: Artificial neural networks versus multiple regressions in tourism demand analysis. J. Travel Res. 38(2), 111–119 (1999)
  17. 17.
    Chae, Y.M., Kim, H.S., Tark, K.C., Park, H.J., Hoa, S.H.: Analysis of healthcare quality indicator using data mining and decision support system. Exp. Syst. Appl. 24, 167–172 (2003)
  18. 18.
    Prather, J.C., Lobach, D.F., Goodwin, L.K., Hales, J.W., Hage, M.L., Hammond, W.E.: Medical DM: knowledge discovery in clinical data warehouse. In: Paper Presented at Annual Conference of AMIA 1997 (1997)

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Avijit Kumar Chaudhuri
    • 1
  • Anirban Das
    • 2
  • Madhumita Addy
    • 3
    Email author
  1. 1.Techno Engineering College BanipurHabraIndia
  2. 2.University of Engineering & ManagementKolkataIndia
  3. 3.Imerit Technology Pvt. LtdKolkataIndia

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