Decision Support Model in Determining Factors and Its Dominant Criteria Affecting Cholesterol Level Based on Rough-Regression

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)

Abstract

The statistical regression models have been frequently used to explain the causal relationship between exogenous factors and the cholesterol level of patients. While, the dominant criteria for each exogenous factor which give impact to the cholesterol level are not yet investigated by previous studies. In this paper, we are interested to introduce a decision making model based on rough-regression in handling the significant contribution between the dominant criteria, exogenous and endogenous factors, respectively. The result showed the proposed model is able to investigate the dominant criteria and factors affecting cholesterol level patients. This model may help the counterparts in the decision making.

Keywords

Rough-regression Decision making Dominant criteria Cholesterol level 

Notes

Acknowledgements

This study is supported by Research, Innovation, Commercialization, and Consultancy Management Office (ORICC) at Universiti Tun Hussein Onn Malaysia (UTHM) and in part by Contract Research Grant Vot. U689.

References

  1. 1.
    Sakurai, M., Stamler, J., Miura, K., Brown, I.J., Nakagawa, H., Elliot, P., Ueshima, H., Chan, Q., Tzoulaki, I., Dyer, A.R., Okayama, A., Zhao, L.: Relationship of dietary cholesterol to blood pressure: the intermap study. J. Hypertens. 29, 222–228 (2011)CrossRefGoogle Scholar
  2. 2.
    Wolk, R., Somers, V.K.: Sleep and the metabolic syndrome. Exp. Physiol. 92, 67–78 (2007)CrossRefGoogle Scholar
  3. 3.
    Kaneita, Y., Uchiyama, M., Yoshiike, N., Ohida, T.: Associations of usual sleep duration with serum lipid and lipoprotein levels. Sleep 31, 645–652 (2008)CrossRefGoogle Scholar
  4. 4.
    Miettinen, T.A.: Cholesterol production in obesity. Circulation 64, 842–850 (1971)CrossRefGoogle Scholar
  5. 5.
    Ueshima, H., Iida, M., Shimamoto, T., Konishi, M., Tanigaki, M., Doi, M., Nakashini, N., Takayama, Y., Ozawa, H., Komachi, Y.: Dietary intake and serum total cholesterol level: their relationship to different lifestyles in several Japanese populations. Circulation 66, 519–526 (1982)CrossRefGoogle Scholar
  6. 6.
    Ali, R., Hussain, J., Siddiqi, M.H., Hussain, M., Lee, S.: A hybrid rough set reasoning model for prediction and management of Diabetes Mellitus. Sensors 15, 15921–15951 (2015)CrossRefGoogle Scholar
  7. 7.
    Pawlak, Z.: Rough sets. Int. J. Compt. Inf. Sci. 11, 341–356 (1982)CrossRefMATHGoogle Scholar
  8. 8.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publisher Dordrecht (1991)Google Scholar
  9. 9.
    Hampton, J.: Rough set theory: the basics (part 1). J. Compt. Intel. Finance 5, 25–29 (1997)Google Scholar
  10. 10.
    Hampton, J.: Rough set theory: the basics (part 1). J. Compt. Intel. Finance 6, 40–42 (1998)Google Scholar
  11. 11.
    Hampton, J.: Rough set theory: the basics (part 1). J. Compt. Intel. Finance 6, 35–37 (1998)Google Scholar
  12. 12.
    Tay, F.E.H., Shen, L.: Economic and financial using rough sets model. Eur. J. Oper. Res. 141, 641–659 (2002)CrossRefMATHGoogle Scholar
  13. 13.
    Herawan, T., Deris, M.M., Abawajy, H.: A rough set approach for selecting clustering attribute. Knowl. Based Syst. 23, 220–231 (2010)CrossRefGoogle Scholar
  14. 14.
    Wooldridge, M.: Introductory Econometrics a Modern Approach, 3rd edn. Thomson, South Western, USA (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Computer ScienceUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia
  2. 2.Mathematics Department, Faculty of Science and TechnologyState Islamic University of Sultan Syarif Kasim RiauPanam, PekanbaruIndonesia

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