Advertisement

Logistic Regression Methods in Selected Medical Information Systems

  • Anna Kasperczuk
  • Agnieszka DardzinskaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10646)

Abstract

This paper presents the process of building a new logistic regression model, which aims to support the decision-making process in medical database. The developed logistic regression model defines the probability of the disease and indicates the statistically significant changes that affect the onset of the disease. The value of probability can be treated as one of the feature in decision process of patient’s future treatment.

Keywords

Selection Logistic regression Decision system Information system 

References

  1. 1.
    Bender, R., Grouven, U.: Logistic regression models used in medical research are poorly presented [Letter]. BMJ 313, 628 (1996)CrossRefGoogle Scholar
  2. 2.
    Campillo, C.: Standardizing criteria for logistic regression models. Ann. Intern. Med. 119, 540–541 (1993)CrossRefGoogle Scholar
  3. 3.
    Chin, S.: The rise and fall of logistic regression. Aust. Epidemiol. 8, 7–10 (2001)Google Scholar
  4. 4.
    Dardzinska, A.: Action Rules Mining. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-35650-6 CrossRefzbMATHGoogle Scholar
  5. 5.
    Hall, G.H., Round, A.P.: Logistic regression: explanation and use. J. R. Coll. Physicians Lond. 28, 242–246 (1994)Google Scholar
  6. 6.
    Harrell, F.: Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer-Verlag, New York (2001). doi: 10.1007/978-3-319-19425-7 zbMATHGoogle Scholar
  7. 7.
    Hosmer, D., Lemeshow, S.: Applied Logistic Regression. Wiley, New Jersey (2000)CrossRefzbMATHGoogle Scholar
  8. 8.
    Jiang, H., Kulkarni, P.M., Mallinckrodt, C.H., Shurzinske, L., Molenberghs, G., Lipkovich, I.: To adjust or not to adjust for baseline when analyzing repeated binary responses? The case of complete data when treatment comparison at study end is of interest. Pharm. Stat. 14, 262–271 (2015)CrossRefGoogle Scholar
  9. 9.
    de Jong, P., Heller, G.Z.: Generalized Linear Models for Insurance Data. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  10. 10.
    Kasperczuk, A., Dardzinska, A.: Comparative evaluation of the different data mining techniques used for the medical database. Acta Mechanica et Automatica 10(3), 233–238 (2016)CrossRefGoogle Scholar
  11. 11.
    Khan, K.S., Chien, P.F., Dwarakanath, L.S.: Logistic regression models in obstetrics and gynecology literature. Obstet. Gynecol. 93, 10014–10020 (1999)Google Scholar
  12. 12.
    Levy, P.S., Stolte, K.: Statistical methods in public health and epidemiology: a look at the recent past and projections for the next decade. Stat. Methods Med. Res. 9, 41–55 (2000)CrossRefzbMATHGoogle Scholar
  13. 13.
    Zhang, Z., Chen, K., Ni, H., et al.: Predictive value of lactate in unselected critically ill patients: an analysis using fractional polynomials. J. Thorac. Dis. 6, 995–1003 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, Division of Biocybernetics and Biomedical EngeeneringBialystok University of TechnologyBialystokPoland

Personalised recommendations