Inductive learning based on rough set theory for medical decision making

Ahmad Taher Azar, Nidhal Bouaynaya, Robi Polikar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

This paper proposes an algorithm that uses inductive learning and rough set theory (ILRS) to analyze the clinical data available in a patient file (records). A typical patient file has unstructured (both descriptive and quantitative) information that is also uncertain and sometimes incomplete. Successful clinical treatments depend on correct medical diagnosis which determines the correct set of variables or features causing a certain pathology. Clinical applications are by no means the only applications that require decision-making with reasoning from a large and incomplete amount of information. We show that the proposed ILRS technique is able to reduce the available number of features into a smaller core set that precisely describes the information system. We can also quantitatively evaluate the level of dependence of the considered pathology, or decision feature, on a given set of condition features or attributes. Moreover, we show that the proposed algorithm is able to cope with uncertain and incomplete information. We consider a case study of an incomplete information system obtained during cannulation of radial and dorsalis pelis arteries. We show how ILRS succeeds to remove redundancy and determine the most significant condition attributes for a given set of decision attributes from contaminated data with uncertainty. A multi-class classification with preference relations is presented through a set of decision rules. Unlike statistical analysis of clinical data, the reliability of the proposed ILRS algorithm is independent of the data size.

Original languageEnglish (US)
Title of host publicationFUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems
EditorsAdnan Yazici, Nikhil R. Pal, Hisao Ishibuchi, Bulent Tutmez, Chin-Teng Lin, Joao M. C. Sousa, Uzay Kaymak, Trevor Martin
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467374286
DOIs
StatePublished - Nov 25 2015
EventIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey
Duration: Aug 2 2015Aug 5 2015

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2015-November
ISSN (Print)1098-7584

Other

OtherIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015
CountryTurkey
CityIstanbul
Period8/2/158/5/15

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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  • Cite this

    Azar, A. T., Bouaynaya, N., & Polikar, R. (2015). Inductive learning based on rough set theory for medical decision making. In A. Yazici, N. R. Pal, H. Ishibuchi, B. Tutmez, C-T. Lin, J. M. C. Sousa, U. Kaymak, & T. Martin (Eds.), FUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems [7338075] (IEEE International Conference on Fuzzy Systems; Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2015.7338075