Automatic diagnosis of acute coronary syndrome using a multi-agent system based in neural networks

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.