Fuzzy Logic Approach to Robust Regression Models of Uncertain Medical Categories

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Dichotomization of the outcome by a single cut-off point is an important part of various medical studies. Usually the relationship between the resulted dichotomized dependent variable and explanatory variables is analyzed with linear regression, probit regression or logistic regression. However, in many real-life situa- tions, a certain cut-off point dividing the outcome into two groups is unknown and can be specified only approximately, i.e. surrounded by some (small) uncertainty. It means that in order to have any practical meaning the regression model must be robust to this uncertainty. In this paper, we show that neither the beta in the linear regression model, nor its significance level is robust to the small variations in the dichotomization cut-off point. As an alternative robust approach to the problem of uncertain medical categories, we propose to use the linear regression model with the fuzzy membership function as a dependent variable. This fuzzy membership function denotes to what degree the value of the underlying (continuous) outcome falls below or above the dichoto- mization cut-off point. In the paper, we demonstrate that the linear regression model of the fuzzy dependent variable can be insensitive against the uncertainty in the cut-off point location. In the paper we present the modeling results from the real study of low hemoglobin levels in infants. We systematically test the robust- ness of the binomial regression model and the linear regression mod- el with the fuzzy dependent variable by changing the boundary for the category Anemia and show that the behavior of the latter model persists over a quite wide interval. Keywords—Categorization, Uncertain medical categories, Bi- nomial regression model, Fuzzy dependent variable, Robustness.
Original languageEnglish
Title of host publicationPROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 16
Pages172-177
Volume16
StatePublished - 2006

Publication series

NameProceedings of World Academy of Science Engineering and Technology

Keywords

  • Categorization
  • Uncertain medical categories
  • Binomial regression model
  • Fuzzy dependent variable
  • Robustness

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