loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Vasilios Zarikas 1 ; Elpiniki Papageorgiou 2 ; Damira Pernebayeva 3 and Nurislam Tursynbek 3

Affiliations: 1 University of Apllied Sciences at Central Greece (TEI of Central Greece) and Nazarbayev University, Greece ; 2 University of Apllied Sciences at Central Greece (TEI of Central Greece), Greece ; 3 Nazarbayev University, Kazakhstan

Keyword(s): Bayesian Networks, Decision Support System, Expert Systems, Fuzzy Rules, Medical Statistics.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bayesian Networks ; Enterprise Information Systems ; Soft Computing

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.141.30.162

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Zarikas, V.; Papageorgiou, E.; Pernebayeva, D. and Tursynbek, N. (2018). Medical Decision Support Tool from a Fuzzy-Rules Driven Bayesian Network. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-275-2; ISSN 2184-433X, SciTePress, pages 539-549. DOI: 10.5220/0006642705390549

@conference{icaart18,
author={Vasilios Zarikas. and Elpiniki Papageorgiou. and Damira Pernebayeva. and Nurislam Tursynbek.},
title={Medical Decision Support Tool from a Fuzzy-Rules Driven Bayesian Network},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2018},
pages={539-549},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006642705390549},
isbn={978-989-758-275-2},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Medical Decision Support Tool from a Fuzzy-Rules Driven Bayesian Network
SN - 978-989-758-275-2
IS - 2184-433X
AU - Zarikas, V.
AU - Papageorgiou, E.
AU - Pernebayeva, D.
AU - Tursynbek, N.
PY - 2018
SP - 539
EP - 549
DO - 10.5220/0006642705390549
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic