A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment

  • Sofia Santos
  • M. Rosário Martins
  • Henrique VicenteEmail author
  • M. Gabriel Barroca
  • Fernando Calisto
  • César Gama
  • Jorge Ribeiro
  • Joana Machado
  • Liliana Ávidos
  • Nuno Araújo
  • Almeida Dias
  • José Neves
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 273)


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%.


Thyroid dysfunction Knowledge Representation and Reasoning Artificial Neural Networks Entropy Logic Programming Many-Valued Empirical Machine 



This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Departamento de Química, Escola de Ciências e TecnologiaUniversidade de ÉvoraÉvoraPortugal
  2. 2.Departamento de Química, Escola de Ciências e Tecnologia, Laboratório HERCULESUniversidade de ÉvoraÉvoraPortugal
  3. 3.Departamento de Química, Escola de Ciências e Tecnologia, Centro de Química de ÉvoraUniversidade de ÉvoraÉvoraPortugal
  4. 4.Centro AlgoritmiUniversidade do MinhoBragaPortugal
  5. 5.SYNLAB AlentejoÉvoraPortugal
  6. 6.Escola Superior de Tecnologia e Gestão, ARC4DigiT – Applied Research Center for Digital TransformationInstituto Politécnico de Viana do CasteloViana do CasteloPortugal
  7. 7.Farmácia de LamaçãesBragaPortugal
  8. 8.CESPUInstituto Universitário de Ciências da SaúdeGandraPortugal

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