Paper Info
Title
A Many-Valued Empirical Machine for Thyroid Dysfunction Assessment.
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%.
Year
DOI
Venue
2018
10.1007/978-3-030-16447-8_5
INTETAIN
Field
DocType
Citations 
Knowledge representation and reasoning,Computer science,Argumentation theory,Decision support system,Thyroid,Intensive care medicine,Multimedia,Thyroid disease
Conference
0
PageRank 
References 
Authors
0.34
0
12
Name
Order
Citations
PageRank
Sofia Santos100.34
M. Rosário Martins200.34
Henrique Vicente32416.93
M. Gabriel Barroca400.34
Fernando Calisto500.34
César Gama600.34
Jorge Ribeiro74812.98
Joana Machado822.11
Liliana Ávidos900.68
Nuno Araújo1000.34
Almeida Dias1101.69
José Machado1220734.92