Medical decision support tool from a fuzzy-rules driven Bayesian network

Vasilios Zarikas, Elpiniki Papageorgiou, Damira Pernebayeva, Nurislam Tursynbek

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

5 Citations (Scopus)

Abstract

The task of carrying out an effective and efficient decision on medical domain is a complex one, since a lot of uncertainty and vagueness is involved. Fuzzy logic and probabilistic methods for handling uncertain and imprecise data both provide an advance towards the goal of constructing an intelligent decision support system (DSS) for medical diagnosis and therapy. This work reports on a successfully developed DSS concerning pneumonia disease. A detailed and clear description of the reasoning behind the core decision making module of the DSS, is included, depicting the proposed methodological issues. The results have shown that the suggested methodology for constructing bayesian networks (BNs) from fuzzy rules gives a front-end decision about the severity of pulmonary infections, providing similar results to those obtained with physicians’ intuition.

Original languageEnglish
Title of host publicationICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence
EditorsJaap van den Herik, Ana Paula Rocha
PublisherSciTePress
Pages539-549
Number of pages11
Volume2
ISBN (Electronic)9789897582752
DOIs
Publication statusPublished - Jan 1 2018
Event10th International Conference on Agents and Artificial Intelligence, ICAART 2018 - Funchal, Madeira, Portugal
Duration: Jan 16 2018Jan 18 2018

Publication series

NameICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence
Volume2

Conference

Conference10th International Conference on Agents and Artificial Intelligence, ICAART 2018
CountryPortugal
CityFunchal, Madeira
Period1/16/181/18/18

Keywords

  • Bayesian Networks
  • Decision Support System
  • Expert Systems
  • Fuzzy Rules
  • Medical Statistics

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence

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