Knowledge and Information Systems

, Volume 34, Issue 3, pp 521–546

Decision rules extraction from data stream in the presence of changing context for diabetes treatment

Authors

    • Institute of Computer Science, Faculty of Computer Science and ManagementWrocław University of Technology
  • Adam Gonczarek
    • Institute of Computer Science, Faculty of Computer Science and ManagementWrocław University of Technology
Open AccessRegular Paper

DOI: 10.1007/s10115-012-0488-7

Cite this article as:
Tomczak, J.M. & Gonczarek, A. Knowl Inf Syst (2013) 34: 521. doi:10.1007/s10115-012-0488-7

Abstract

The knowledge extraction is an important element of the e-Health system. In this paper, we introduce a new method for decision rules extraction called Graph-based Rules Inducer to support the medical interview in the diabetes treatment. The emphasis is put on the capability of hidden context change tracking. The context is understood as a set of all factors affecting patient condition. In order to follow context changes, a forgetting mechanism with a forgetting factor is implemented in the proposed algorithm. Moreover, to aggregate data, a graph representation is used and a limitation of the search space is proposed to protect from overfitting. We demonstrate the advantages of our approach in comparison with other methods through an empirical study on the Electricity benchmark data set in the classification task. Subsequently, our method is applied in the diabetes treatment as a tool supporting medical interviews.

Keywords

Decision rulesForgettingIncremental learningHidden contextDiabetes
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Copyright information

© The Author(s) 2012