Serviços Personalizados
Journal
Artigo
Indicadores
Citado por SciELO
Acessos
Links relacionados
Citado por Google
Similares em SciELO
Similares em Google
Compartilhar
Revista Colombiana de Cardiología
versão impressa ISSN 0120-5633
Resumo
DIAZ, John Jaime Sprockel; FERNANDEZ, Juan José Diaztagle e GUERRERO, Enrique González. Automatic diagnosis of acute coronary syndrome using a multi-agent system based in neural networks. Rev. Colomb. Cardiol. [online]. 2017, vol.24, n.3, pp.255-260. Epub 28-Abr-2017. ISSN 0120-5633. https://doi.org/10.1016/j.rccar.2016.11.010.
Introduction:
Because it is a highly complex task of a great clinical importance, the diagnosis of acute coronary syndromes allows for their analysis by means of intelligent system models. Motivation: To develop a multi-agent system that assembles the decisions of several neural networks for the diagnosis of chest pain with a focus on acute coronary syndromes.
Methods:
A study of diagnostic tests where a series of neural networks are trained with a precision close to 70%, and are later on assembled with three voting systems. Then the results of special networks on specific populations are added to select the best configuration that Will make part of a multi-agent system for diagnosing chest pain.
Results:
A total of 84 networks were generated, with an average precision of 72% during testing; once assembled this precision rises up to a maximum of 84%, which then reaches 89% when the special groups are included. A configuration that offers a sensitivity of 96% with a specificity of 77% and positive and negative predictive values of 87 and 93% respectively is chosen for the diagnosis of acute coronary syndrome.
Conclusions:
It is possible to develop a tool for the automatic diagnosis of acute coronary syndrome using a multi-agent system that assembles the dispositions taken by a set of artificial neural networks. Its performance allows taking it into consideration for implementing it within a clinical decision-making support system.
Palavras-chave : Diagnosis; Acute coronary syndrome; Acute myocardial infarction; Unstable angina; Chest pain.