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Revista Colombiana de Cardiología

Print version ISSN 0120-5633

Abstract

DIAZ, John Jaime Sprockel; FERNANDEZ, Juan José Diaztagle  and  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 Apr 28, 2017. ISSN 0120-5633.  http://dx.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.

Keywords : Diagnosis; Acute coronary syndrome; Acute myocardial infarction; Unstable angina; Chest pain.

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