@inproceedings{420536d0bc9a45649b84d3792760f437,
title = "Medical decision support tool from a fuzzy-rules driven Bayesian network",
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{\textquoteright} intuition.",
keywords = "Bayesian Networks, Decision Support System, Expert Systems, Fuzzy Rules, Medical Statistics",
author = "Vasilios Zarikas and Elpiniki Papageorgiou and Damira Pernebayeva and Nurislam Tursynbek",
note = "Publisher Copyright: Copyright {\textcopyright} 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 10th International Conference on Agents and Artificial Intelligence, ICAART 2018 ; Conference date: 16-01-2018 Through 18-01-2018",
year = "2018",
month = jan,
day = "1",
doi = "10.5220/0006642705390549",
language = "English",
volume = "2",
series = "ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence",
publisher = "SciTePress",
pages = "539--549",
editor = "\{van den Herik\}, Jaap and Rocha, \{Ana Paula\}",
booktitle = "ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence",
}