Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/25496

Title: A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment
Authors: Santos, Sofia
Martins, M. Rosário
Vicente, Henrique
Barroca, M. Gabriel
Calisto, Fernando
Gama, César
Ribeiro, Jorge
Machado, Joana
Ávidos, Liliana
Araújo, Nuno
Dias, Almeida
Neves, José
Keywords: Thyroid Dysfunction
Knowledge Representation and Reasoning
Artificial Neural Networks
Entropy
Logic Programming
Many-Valued Empirical Machine
Issue Date: 2019
Publisher: Springer
Citation: Santos, S., Martins, M.R., Vicente, H., Barroca, M.G., Calisto, F., Gama, C., Ribeiro, J., Machado, J., Ávidos, L., Araújo, N., Dias, A. and Neves, J. A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 273, 47–57, 2019.
Abstract: Thyroid Dysfunction is a clinical condition that affects thyroid behaviour and is reported to be the most common in all endocrine disorders. It is a multiple factorial pathology condition due to the high incidence of hypothyroidism and hyperthyroidism, which is becoming a serious health problem requiring a detailed study for early diagnosis and monitoring. Understanding the prevalence and risk factors of thyroid disease can be very useful to identify patients for screening and/or follow-up and to minimize their collateral effects. Thus, this paper describes the development of a decision support system that aims to help physicians in the decision-making process regarding thyroid dysfunction assessment. The proposed problem-solving method is based on a symbolic/sub-symbolic line of logical formalisms that have been articulated as an Artificial Neural Network approach to data processing, complemented by an unusual approach to Knowledge Representation and Argumentation that takes into account the data elements entropic states. The model performs well in the thyroid dysfunction assessment with an accuracy ranging between 93.2% and 96.9%.
URI: https://link.springer.com/chapter/10.1007/978-3-030-16447-8_5
http://hdl.handle.net/10174/25496
ISSN: 1867-8211 (paper)
1867-822X (electronic)
Type: article
Appears in Collections:QUI - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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